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1.1: The Working Hypothesis

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Using the scientific method, before any statistical analysis can be conducted, a researcher must generate a guess, or hypothesis about what is going on. The process begins with a Working Hypothesis . This is a direct statement of the research idea. For example, a plant biologist may think that plant height may be affected by applying different fertilizers. So they might say: " Plants with different fertilizers will grow to different heights ".

But according to the Popperian Principle of Falsification, we can't conclusively affirm a hypothesis, but we can conclusively negate a hypothesis. So we need to translate the working hypothesis into a framework wherein we state a null hypothesis that the average height (or mean height) for plants with the different fertilizers will all be the same. The alternative hypothesis (which the biologist hopes to show) is that they are not all equal, but rather some of the fertilizer treatments have produced plants with different mean heights. The strength of the data will determine whether the null hypothesis can be rejected with a specified level of confidence.

Pictured in the graph below, we can imagine testing three kinds of fertilizer and also one group of plants that are untreated (the control). The plant biologist kept all the plants under controlled conditions in the greenhouse, to focus on the effect of the fertilizer, the only thing we know to differ among the plants. At the end of the experiment, the biologist measured the height of each plant. Plant height is the dependent or response variable and is plotted on the vertical (\(y\)) axis. The biologist used a simple boxplot to plot the difference in the heights.

This boxplot is a customary way to show treatment (or factor) level differences. In this case, there was only one treatment: fertilizer. The fertilizer treatment had four levels that included the control, which received no fertilizer. Using this language convention is important because later on we will be using ANOVA to handle multi-factor studies (for example if the biologist manipulated the amount of water AND the type of fertilizer) and we will need to be able to refer to different treatments, each with their own set of levels.

Another alternative for viewing the differences in the heights is with a means plot (a scatter or interval plot):

This second method to plot the difference in the means of the treatments provides essentially the same information. However, this plot illustrates the variability in the data with 'error bars' that are the 95% confidence interval limits around the means.

In between the statement of a Working Hypothesis and the creation of the 95% confidence intervals used to create this means plot is a 7-step process of statistical hypothesis testing, presented in the following section.

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1.1 - the working hypothesis.

Using the scientific method, before any statistical analysis can be conducted, a researcher must generate a guess, or hypothesis about what is going on. The process begins with a Working Hypothesis . This is a direct statement of the research idea. For example, a plant biologist may think that plant height may be affected by applying different fertilizers. So they might say: " Plants with different fertilizers will grow to different heights ".

According to the Popperian Principle of Falsification, we can't conclusively affirm a hypothesis, but we can conclusively negate a hypothesis. So we need to translate the working hypothesis into a framework wherein we state a null hypothesis that the average height (or mean height) for plants with the different fertilizers will all be the same. The alternative hypothesis (which the biologist hopes to show) is that they are not all equal, but rather some of the fertilizer treatments have produced plants with different mean heights. The strength of the data will determine whether the null hypothesis can be rejected with a specified level of confidence.

We can imagine testing 4 groups of plants, three with three different kinds of fertilizer and the fourth untreated (a control group). Assuming the plant biologist kept all the plants under controlled conditions in the greenhouse, the effect of the fertilizer would be the only thing to differ among the groups of plants. Suppose at the end of the experiment, the biologist measured the height of each plant. A simple boxplot can then be used to illustrate the difference in the heights between the four groups, seen in the figure below. Plant height, the dependent or response variable, is seen on the vertical (y) axis versus fertilizer, the independent or explanatory variable, seen on the horizontal (x) axis.

This boxplot is a customary way to show treatment (or factor) level differences. In this case, there was only one treatment: fertilizer. The fertilizer treatment had four levels that included the control, which received no fertilizer, and the three different fertilizers. Understanding this language convention is essential as later in the course we will be using ANOVA to handle multi-factor studies (for example if the biologist manipulated the amount of water AND the type of fertilizer) and we will need to be able to refer to different treatments, each with their own set of levels.

Another alternative for viewing the differences in the heights is with a 'means plot' (a scatter or interval plot):

This second plotting method for the differences in the treatment means provides essentially the same information. However, this plot illustrates the variability in the data with "error bars" that are the 95% confidence interval limits around the means. Between the statement of a Working Hypothesis and the creation of these 95% confidence intervals is a 7-step process of statistical hypothesis testing, presented in the following section.

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The potential of working hypotheses for deductive exploratory research

Mattia casula.

1 Department of Political and Social Sciences, University of Bologna, Strada Maggiore 45, 40125 Bologna, Italy

Nandhini Rangarajan

2 Texas State University, San Marcos, TX USA

Patricia Shields

While hypotheses frame explanatory studies and provide guidance for measurement and statistical tests, deductive, exploratory research does not have a framing device like the hypothesis. To this purpose, this article examines the landscape of deductive, exploratory research and offers the working hypothesis as a flexible, useful framework that can guide and bring coherence across the steps in the research process. The working hypothesis conceptual framework is introduced, placed in a philosophical context, defined, and applied to public administration and comparative public policy. Doing so, this article explains: the philosophical underpinning of exploratory, deductive research; how the working hypothesis informs the methodologies and evidence collection of deductive, explorative research; the nature of micro-conceptual frameworks for deductive exploratory research; and, how the working hypothesis informs data analysis when exploratory research is deductive.

Introduction

Exploratory research is generally considered to be inductive and qualitative (Stebbins 2001 ). Exploratory qualitative studies adopting an inductive approach do not lend themselves to a priori theorizing and building upon prior bodies of knowledge (Reiter 2013 ; Bryman 2004 as cited in Pearse 2019 ). Juxtaposed against quantitative studies that employ deductive confirmatory approaches, exploratory qualitative research is often criticized for lack of methodological rigor and tentativeness in results (Thomas and Magilvy 2011 ). This paper focuses on the neglected topic of deductive, exploratory research and proposes working hypotheses as a useful framework for these studies.

To emphasize that certain types of applied research lend themselves more easily to deductive approaches, to address the downsides of exploratory qualitative research, and to ensure qualitative rigor in exploratory research, a significant body of work on deductive qualitative approaches has emerged (see for example, Gilgun 2005 , 2015 ; Hyde 2000 ; Pearse 2019 ). According to Gilgun ( 2015 , p. 3) the use of conceptual frameworks derived from comprehensive reviews of literature and a priori theorizing were common practices in qualitative research prior to the publication of Glaser and Strauss’s ( 1967 ) The Discovery of Grounded Theory . Gilgun ( 2015 ) coined the terms Deductive Qualitative Analysis (DQA) to arrive at some sort of “middle-ground” such that the benefits of a priori theorizing (structure) and allowing room for new theory to emerge (flexibility) are reaped simultaneously. According to Gilgun ( 2015 , p. 14) “in DQA, the initial conceptual framework and hypotheses are preliminary. The purpose of DQA is to come up with a better theory than researchers had constructed at the outset (Gilgun 2005 , 2009 ). Indeed, the production of new, more useful hypotheses is the goal of DQA”.

DQA provides greater level of structure for both the experienced and novice qualitative researcher (see for example Pearse 2019 ; Gilgun 2005 ). According to Gilgun ( 2015 , p. 4) “conceptual frameworks are the sources of hypotheses and sensitizing concepts”. Sensitizing concepts frame the exploratory research process and guide the researcher’s data collection and reporting efforts. Pearse ( 2019 ) discusses the usefulness for deductive thematic analysis and pattern matching to help guide DQA in business research. Gilgun ( 2005 ) discusses the usefulness of DQA for family research.

Given these rationales for DQA in exploratory research, the overarching purpose of this paper is to contribute to that growing corpus of work on deductive qualitative research. This paper is specifically aimed at guiding novice researchers and student scholars to the working hypothesis as a useful a priori framing tool. The applicability of the working hypothesis as a tool that provides more structure during the design and implementation phases of exploratory research is discussed in detail. Examples of research projects in public administration that use the working hypothesis as a framing tool for deductive exploratory research are provided.

In the next section, we introduce the three types of research purposes. Second, we examine the nature of the exploratory research purpose. Third, we provide a definition of working hypothesis. Fourth, we explore the philosophical roots of methodology to see where exploratory research fits. Fifth, we connect the discussion to the dominant research approaches (quantitative, qualitative and mixed methods) to see where deductive exploratory research fits. Sixth, we examine the nature of theory and the role of the hypothesis in theory. We contrast formal hypotheses and working hypotheses. Seven, we provide examples of student and scholarly work that illustrates how working hypotheses are developed and operationalized. Lastly, this paper synthesizes previous discussion with concluding remarks.

Three types of research purposes

The literature identifies three basic types of research purposes—explanation, description and exploration (Babbie 2007 ; Adler and Clark 2008 ; Strydom 2013 ; Shields and Whetsell 2017 ). Research purposes are similar to research questions; however, they focus on project goals or aims instead of questions.

Explanatory research answers the “why” question (Babbie 2007 , pp. 89–90), by explaining “why things are the way they are”, and by looking “for causes and reasons” (Adler and Clark 2008 , p. 14). Explanatory research is closely tied to hypothesis testing. Theory is tested using deductive reasoning, which goes from the general to the specific (Hyde 2000 , p. 83). Hypotheses provide a frame for explanatory research connecting the research purpose to other parts of the research process (variable construction, choice of data, statistical tests). They help provide alignment or coherence across stages in the research process and provide ways to critique the strengths and weakness of the study. For example, were the hypotheses grounded in the appropriate arguments and evidence in the literature? Are the concepts imbedded in the hypotheses appropriately measured? Was the best statistical test used? When the analysis is complete (hypothesis is tested), the results generally answer the research question (the evidence supported or failed to support the hypothesis) (Shields and Rangarajan 2013 ).

Descriptive research addresses the “What” question and is not primarily concerned with causes (Strydom 2013 ; Shields and Tajalli 2006 ). It lies at the “midpoint of the knowledge continuum” (Grinnell 2001 , p. 248) between exploration and explanation. Descriptive research is used in both quantitative and qualitative research. A field researcher might want to “have a more highly developed idea of social phenomena” (Strydom 2013 , p. 154) and develop thick descriptions using inductive logic. In science, categorization and classification systems such as the periodic table of chemistry or the taxonomies of biology inform descriptive research. These baseline classification systems are a type of theorizing and allow researchers to answer questions like “what kind” of plants and animals inhabit a forest. The answer to this question would usually be displayed in graphs and frequency distributions. This is also the data presentation system used in the social sciences (Ritchie and Lewis 2003 ; Strydom 2013 ). For example, if a scholar asked, what are the needs of homeless people? A quantitative approach would include a survey that incorporated a “needs” classification system (preferably based on a literature review). The data would be displayed as frequency distributions or as charts. Description can also be guided by inductive reasoning, which draws “inferences from specific observable phenomena to general rules or knowledge expansion” (Worster 2013 , p. 448). Theory and hypotheses are generated using inductive reasoning, which begins with data and the intention of making sense of it by theorizing. Inductive descriptive approaches would use a qualitative, naturalistic design (open ended interview questions with the homeless population). The data could provide a thick description of the homeless context. For deductive descriptive research, categories, serve a purpose similar to hypotheses for explanatory research. If developed with thought and a connection to the literature, categories can serve as a framework that inform measurement, link to data collection mechanisms and to data analysis. Like hypotheses they can provide horizontal coherence across the steps in the research process.

Table  1 demonstrated these connections for deductive, descriptive and explanatory research. The arrow at the top emphasizes the horizontal or across the research process view we emphasize. This article makes the case that the working hypothesis can serve the same purpose as the hypothesis for deductive, explanatory research and categories for deductive descriptive research. The cells for exploratory research are filled in with question marks.

Table 1

Connecting research purpose and frameworks for deductive inquiry

The remainder of this paper focuses on exploratory research and the answers to questions found in the table:

• What is the philosophical underpinning of exploratory, deductive research?
• What is the Micro-conceptual framework for deductive exploratory research? [ As is clear from the article title we introduce the working hypothesis as the answer .]
• How does the working hypothesis inform the methodologies and evidence collection of deductive exploratory research?
• How does the working hypothesis inform data analysis of deductive exploratory research?

The nature of exploratory research purpose

Explorers enter the unknown to discover something new. The process can be fraught with struggle and surprises. Effective explorers creatively resolve unexpected problems. While we typically think of explorers as pioneers or mountain climbers, exploration is very much linked to the experience and intention of the explorer. Babies explore as they take their first steps. The exploratory purpose resonates with these insights. Exploratory research, like reconnaissance, is a type of inquiry that is in the preliminary or early stages (Babbie 2007 ). It is associated with discovery, creativity and serendipity (Stebbins 2001 ). But the person doing the discovery, also defines the activity or claims the act of exploration. It “typically occurs when a researcher examines a new interest or when the subject of study itself is relatively new” (Babbie 2007 , p. 88). Hence, exploration has an open character that emphasizes “flexibility, pragmatism, and the particular, biographically specific interests of an investigator” (Maanen et al. 2001 , p. v). These three purposes form a type of hierarchy. An area of inquiry is initially explored . This early work lays the ground for, description which in turn becomes the basis for explanation . Quantitative, explanatory studies dominate contemporary high impact journals (Twining et al. 2017 ).

Stebbins ( 2001 ) makes the point that exploration is often seen as something like a poor stepsister to confirmatory or hypothesis testing research. He has a problem with this because we live in a changing world and what is settled today will very likely be unsettled in the near future and in need of exploration. Further, exploratory research “generates initial insights into the nature of an issue and develops questions to be investigated by more extensive studies” (Marlow 2005 , p. 334). Exploration is widely applicable because all research topics were once “new.” Further, all research topics have the possibility of “innovation” or ongoing “newness”. Exploratory research may be appropriate to establish whether a phenomenon exists (Strydom 2013 ). The point here, of course, is that the exploratory purpose is far from trivial.

Stebbins’ Exploratory Research in the Social Sciences ( 2001 ), is the only book devoted to the nature of exploratory research as a form of social science inquiry. He views it as a “broad-ranging, purposive, systematic prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life” (p. 3). It is science conducted in a way distinct from confirmation. According to Stebbins ( 2001 , p. 6) the goal is discovery of potential generalizations, which can become future hypotheses and eventually theories that emerge from the data. He focuses on inductive logic (which stimulates creativity) and qualitative methods. He does not want exploratory research limited to the restrictive formulas and models he finds in confirmatory research. He links exploratory research to Glaser and Strauss’s ( 1967 ) flexible, immersive, Grounded Theory. Strydom’s ( 2013 ) analysis of contemporary social work research methods books echoes Stebbins’ ( 2001 ) position. Stebbins’s book is an important contribution, but it limits the potential scope of this flexible and versatile research purpose. If we accepted his conclusion, we would delete the “Exploratory” row from Table  1 .

Note that explanatory research can yield new questions, which lead to exploration. Inquiry is a process where inductive and deductive activities can occur simultaneously or in a back and forth manner, particularly as the literature is reviewed and the research design emerges. 1 Strict typologies such as explanation, description and exploration or inductive/deductive can obscures these larger connections and processes. We draw insight from Dewey’s ( 1896 ) vision of inquiry as depicted in his seminal “Reflex Arc” article. He notes that “stimulus” and “response” like other dualities (inductive/deductive) exist within a larger unifying system. Yet the terms have value. “We need not abandon terms like stimulus and response, so long as we remember that they are attached to events based upon their function in a wider dynamic context, one that includes interests and aims” (Hildebrand 2008 , p. 16). So too, in methodology typologies such as deductive/inductive capture useful distinctions with practical value and are widely used in the methodology literature.

We argue that there is a role for exploratory, deductive, and confirmatory research. We maintain all types of research logics and methods should be in the toolbox of exploratory research. First, as stated above, it makes no sense on its face to identify an extremely flexible purpose that is idiosyncratic to the researcher and then basically restrict its use to qualitative, inductive, non-confirmatory methods. Second, Stebbins’s ( 2001 ) work focused on social science ignoring the policy sciences. Exploratory research can be ideal for immediate practical problems faced by policy makers, who could find a framework of some kind useful. Third, deductive, exploratory research is more intentionally connected to previous research. Some kind of initial framing device is located or designed using the literature. This may be very important for new scholars who are developing research skills and exploring their field and profession. Stebbins’s insights are most pertinent for experienced scholars. Fourth, frameworks and deductive logic are useful for comparative work because some degree of consistency across cases is built into the design.

As we have seen, the hypotheses of explanatory and categories of descriptive research are the dominate frames of social science and policy science. We certainly concur that neither of these frames makes a lot of sense for exploratory research. They would tend to tie it down. We see the problem as a missing framework or missing way to frame deductive, exploratory research in the methodology literature. Inductive exploratory research would not work for many case studies that are trying to use evidence to make an argument. What exploratory deductive case studies need is a framework that incorporates flexibility. This is even more true for comparative case studies. A framework of this sort could be usefully applied to policy research (Casula 2020a ), particularly evaluative policy research, and applied research generally. We propose the Working Hypothesis as a flexible conceptual framework and as a useful tool for doing exploratory studies. It can be used as an evaluative criterion particularly for process evaluation and is useful for student research because students can develop theorizing skills using the literature.

Table  1 included a column specifying the philosophical basis for each research purpose. Shifting gears to the philosophical underpinning of methodology provides useful additional context for examination of deductive, exploratory research.

What is a working hypothesis

The working hypothesis is first and foremost a hypothesis or a statement of expectation that is tested in action. The term “working” suggest that these hypotheses are subject to change, are provisional and the possibility of finding contradictory evidence is real. In addition, a “working” hypothesis is active, it is a tool in an ongoing process of inquiry. If one begins with a research question, the working hypothesis could be viewed as a statement or group of statements that answer the question. It “works” to move purposeful inquiry forward. “Working” also implies some sort of community, mostly we work together in relationship to achieve some goal.

Working Hypothesis is a term found in earlier literature. Indeed, both pioneering pragmatists, John Dewey and George Herbert Mead use the term working hypothesis in important nineteenth century works. For both Dewey and Mead, the notion of a working hypothesis has a self-evident quality and it is applied in a big picture context. 2

Most notably, Dewey ( 1896 ), in one of his most pivotal early works (“Reflex Arc”), used “working hypothesis” to describe a key concept in psychology. “The idea of the reflex arc has upon the whole come nearer to meeting this demand for a general working hypothesis than any other single concept (Italics added)” (p. 357). The notion of a working hypothesis was developed more fully 42 years later, in Logic the Theory of Inquiry , where Dewey developed the notion of a working hypothesis that operated on a smaller scale. He defines working hypotheses as a “provisional, working means of advancing investigation” (Dewey 1938 , pp. 142). Dewey’s definition suggests that working hypotheses would be useful toward the beginning of a research project (e.g., exploratory research).

Mead ( 1899 ) used working hypothesis in a title of an American Journal of Sociology article “The Working Hypothesis and Social Reform” (italics added). He notes that a scientist’s foresight goes beyond testing a hypothesis.

Given its success, he may restate his world from this standpoint and get the basis for further investigation that again always takes the form of a problem. The solution of this problem is found over again in the possibility of fitting his hypothetical proposition into the whole within which it arises. And he must recognize that this statement is only a working hypothesis at the best, i.e., he knows that further investigation will show that the former statement of his world is only provisionally true, and must be false from the standpoint of a larger knowledge, as every partial truth is necessarily false over against the fuller knowledge which he will gain later (Mead 1899 , p. 370).

Cronbach ( 1975 ) developed a notion of working hypothesis consistent with inductive reasoning, but for him, the working hypothesis is a product or result of naturalistic inquiry. He makes the case that naturalistic inquiry is highly context dependent and therefore results or seeming generalizations that may come from a study and should be viewed as “working hypotheses”, which “are tentative both for the situation in which they first uncovered and for other situations” (as cited in Gobo 2008 , p. 196).

A quick Google scholar search using the term “working hypothesis” show that it is widely used in twentieth and twenty-first century science, particularly in titles. In these articles, the working hypothesis is treated as a conceptual tool that furthers investigation in its early or transitioning phases. We could find no explicit links to exploratory research. The exploratory nature of the problem is expressed implicitly. Terms such as “speculative” (Habib 2000 , p. 2391) or “rapidly evolving field” (Prater et al. 2007 , p. 1141) capture the exploratory nature of the study. The authors might describe how a topic is “new” or reference “change”. “As a working hypothesis, the picture is only new, however, in its interpretation” (Milnes 1974 , p. 1731). In a study of soil genesis, Arnold ( 1965 , p. 718) notes “Sequential models, formulated as working hypotheses, are subject to further investigation and change”. Any 2020 article dealing with COVID-19 and respiratory distress would be preliminary almost by definition (Ciceri et al. 2020 ).

Philosophical roots of methodology

According to Kaplan ( 1964 , p. 23) “the aim of methodology is to help us understand, in the broadest sense not the products of scientific inquiry but the process itself”. Methods contain philosophical principles that distinguish them from other “human enterprises and interests” (Kaplan 1964 , p. 23). Contemporary research methodology is generally classified as quantitative, qualitative and mixed methods. Leading scholars of methodology have associated each with a philosophical underpinning—positivism (or post-positivism), interpretivism or constructivist and pragmatism, respectively (Guba 1987 ; Guba and Lincoln 1981 ; Schrag 1992 ; Stebbins 2001 ; Mackenzi and Knipe 2006 ; Atieno 2009 ; Levers 2013 ; Morgan 2007 ; O’Connor et al. 2008 ; Johnson and Onwuegbuzie 2004 ; Twining et al. 2017 ). This section summarizes how the literature often describes these philosophies and informs contemporary methodology and its literature.

Positivism and its more contemporary version, post-positivism, maintains an objectivist ontology or assumes an objective reality, which can be uncovered (Levers 2013 ; Twining et al. 2017 ). 3 Time and context free generalizations are possible and “real causes of social scientific outcomes can be determined reliably and validly (Johnson and Onwuegbunzie 2004 , p. 14). Further, “explanation of the social world is possible through a logical reduction of social phenomena to physical terms”. It uses an empiricist epistemology which “implies testability against observation, experimentation, or comparison” (Whetsell and Shields 2015 , pp. 420–421). Correspondence theory, a tenet of positivism, asserts that “to each concept there corresponds a set of operations involved in its scientific use” (Kaplan 1964 , p. 40).

The interpretivist, constructivists or post-modernist approach is a reaction to positivism. It uses a relativist ontology and a subjectivist epistemology (Levers 2013 ). In this world of multiple realities, context free generalities are impossible as is the separation of facts and values. Causality, explanation, prediction, experimentation depend on assumptions about the correspondence between concepts and reality, which in the absence of an objective reality is impossible. Empirical research can yield “contextualized emergent understanding rather than the creation of testable theoretical structures” (O’Connor et al. 2008 , p. 30). The distinctively different world views of positivist/post positivist and interpretivist philosophy is at the core of many controversies in methodology, social and policy science literature (Casula 2020b ).

With its focus on dissolving dualisms, pragmatism steps outside the objective/subjective debate. Instead, it asks, “what difference would it make to us if the statement were true” (Kaplan 1964 , p. 42). Its epistemology is connected to purposeful inquiry. Pragmatism has a “transformative, experimental notion of inquiry” anchored in pluralism and a focus on constructing conceptual and practical tools to resolve “problematic situations” (Shields 1998 ; Shields and Rangarajan 2013 ). Exploration and working hypotheses are most comfortably situated within the pragmatic philosophical perspective.

Research approaches

Empirical investigation relies on three types of methodology—quantitative, qualitative and mixed methods.

Quantitative methods

Quantitative methods uses deductive logic and formal hypotheses or models to explain, predict, and eventually establish causation (Hyde 2000 ; Kaplan 1964 ; Johnson and Onwuegbunzie 2004 ; Morgan 2007 ). 4 The correspondence between the conceptual and empirical world make measures possible. Measurement assigns numbers to objects, events or situations and allows for standardization and subtle discrimination. It also allows researchers to draw on the power of mathematics and statistics (Kaplan 1964 , pp. 172–174). Using the power of inferential statistics, quantitative research employs research designs, which eliminate competing hypotheses. It is high in external validity or the ability to generalize to the whole. The research results are relatively independent of the researcher (Johnson & Onwuegbunzie 2004 ).

Quantitative methods depend on the quality of measurement and a priori conceptualization, and adherence to the underlying assumptions of inferential statistics. Critics charge that hypotheses and frameworks needlessly constrain inquiry (Johnson and Onwuegbunzie 2004 , p. 19). Hypothesis testing quantitative methods support the explanatory purpose.

Qualitative methods

Qualitative researchers who embrace the post-modern, interpretivist view, 5 question everything about the nature of quantitative methods (Willis et al. 2007 ). Rejecting the possibility of objectivity, correspondence between ideas and measures, and the constraints of a priori theorizing they focus on “unique impressions and understandings of events rather than to generalize the findings” (Kolb 2012 , p. 85). Characteristics of traditional qualitative research include “induction, discovery, exploration, theory/hypothesis generation and the researcher as the primary ‘instrument’ of data collection” (Johnson and Onwuegbunzie 2004 , p. 18). It also concerns itself with forming “unique impressions and understandings of events rather than to generalize findings” (Kolb 2012 , p. 85). The data of qualitative methods are generated via interviews, direct observation, focus groups and analysis of written records or artifacts.

Qualitative methods provide for understanding and “description of people’s personal experiences of phenomena”. They enable descriptions of detailed “phenomena as they are situated and embedded in local contexts.” Researchers use naturalistic settings to “study dynamic processes” and explore how participants interpret experiences. Qualitative methods have an inherent flexibility, allowing researchers to respond to changes in the research setting. They are particularly good at narrowing to the particular and on the flipside have limited external validity (Johnson and Onwuegbunzie 2004 , p. 20). Instead of specifying a suitable sample size to draw conclusions, qualitative research uses the notion of saturation (Morse 1995 ).

Saturation is used in grounded theory—a widely used and respected form of qualitative research, and a well-known interpretivist qualitative research method. Introduced by Glaser and Strauss ( 1967 ), this “grounded on observation” (Patten and Newhart 2000 , p. 27) methodology, focuses on “the creation of emergent understanding” (O’Connor et al. 2008 , p. 30). It uses the Constant Comparative method, whereby researchers develop theory from data as they code and analyze at the same time. Data collection, coding and analysis along with theoretical sampling are systematically combined to generate theory (Kolb 2012 , p. 83). The qualitative methods discussed here support exploratory research.

A close look at the two philosophies and assumptions of quantitative and qualitative research suggests two contradictory world views. The literature has labeled these contradictory views the Incompatibility Theory, which sets up a quantitative versus qualitative tension similar to the seeming separation of art and science or fact and values (Smith 1983a , b ; Guba 1987 ; Smith and Heshusius 1986 ; Howe 1988 ). The incompatibility theory does not make sense in practice. Yin ( 1981 , 1992 , 2011 , 2017 ), a prominent case study scholar, showcases a deductive research methodology that crosses boundaries using both quantaitive and qualitative evidence when appropriate.

Mixed methods

Turning the “Incompatibility Theory” on its head, Mixed Methods research “combines elements of qualitative and quantitative research approaches … for the broad purposes of breadth and depth of understanding and corroboration” (Johnson et al. 2007 , p. 123). It does this by partnering with philosophical pragmatism. 6 Pragmatism is productive because “it offers an immediate and useful middle position philosophically and methodologically; it offers a practical and outcome-oriented method of inquiry that is based on action and leads, iteratively, to further action and the elimination of doubt; it offers a method for selecting methodological mixes that can help researchers better answer many of their research questions” (Johnson and Onwuegbunzie 2004 , p. 17). What is theory for the pragmatist “any theoretical model is for the pragmatist, nothing more than a framework through which problems are perceived and subsequently organized ” (Hothersall 2019 , p. 5).

Brendel ( 2009 ) constructed a simple framework to capture the core elements of pragmatism. Brendel’s four “p”’s—practical, pluralism, participatory and provisional help to show the relevance of pragmatism to mixed methods. Pragmatism is purposeful and concerned with the practical consequences. The pluralism of pragmatism overcomes quantitative/qualitative dualism. Instead, it allows for multiple perspectives (including positivism and interpretivism) and, thus, gets around the incompatibility problem. Inquiry should be participatory or inclusive of the many views of participants, hence, it is consistent with multiple realities and is also tied to the common concern of a problematic situation. Finally, all inquiry is provisional . This is compatible with experimental methods, hypothesis testing and consistent with the back and forth of inductive and deductive reasoning. Mixed methods support exploratory research.

Advocates of mixed methods research note that it overcomes the weaknesses and employs the strengths of quantitative and qualitative methods. Quantitative methods provide precision. The pictures and narrative of qualitative techniques add meaning to the numbers. Quantitative analysis can provide a big picture, establish relationships and its results have great generalizability. On the other hand, the “why” behind the explanation is often missing and can be filled in through in-depth interviews. A deeper and more satisfying explanation is possible. Mixed-methods brings the benefits of triangulation or multiple sources of evidence that converge to support a conclusion. It can entertain a “broader and more complete range of research questions” (Johnson and Onwuegbunzie 2004 , p. 21) and can move between inductive and deductive methods. Case studies use multiple forms of evidence and are a natural context for mixed methods.

One thing that seems to be missing from mixed method literature and explicit design is a place for conceptual frameworks. For example, Heyvaert et al. ( 2013 ) examined nine mixed methods studies and found an explicit framework in only two studies (transformative and pragmatic) (p. 663).

Theory and hypotheses: where is and what is theory?

Theory is key to deductive research. In essence, empirical deductive methods test theory. Hence, we shift our attention to theory and the role and functions of the hypotheses in theory. Oppenheim and Putnam ( 1958 ) note that “by a ‘theory’ (in the widest sense) we mean any hypothesis, generalization or law (whether deterministic or statistical) or any conjunction of these” (p. 25). Van Evera ( 1997 ) uses a similar and more complex definition “theories are general statements that describe and explain the causes of effects of classes of phenomena. They are composed of causal laws or hypotheses, explanations, and antecedent conditions” (p. 8). Sutton and Staw ( 1995 , p. 376) in a highly cited article “What Theory is Not” assert the that hypotheses should contain logical arguments for “why” the hypothesis is expected. Hypotheses need an underlying causal argument before they can be considered theory. The point of this discussion is not to define theory but to establish the importance of hypotheses in theory.

Explanatory research is implicitly relational (A explains B). The hypotheses of explanatory research lay bare these relationships. Popular definitions of hypotheses capture this relational component. For example, the Cambridge Dictionary defines a hypothesis a “an idea or explanation for something that is based on known facts but has not yet been proven”. Vocabulary.Com’s definition emphasizes explanation, a hypothesis is “an idea or explanation that you then test through study and experimentation”. According to Wikipedia a hypothesis is “a proposed explanation for a phenomenon”. Other definitions remove the relational or explanatory reference. The Oxford English Dictionary defines a hypothesis as a “supposition or conjecture put forth to account for known facts.” Science Buddies defines a hypothesis as a “tentative, testable answer to a scientific question”. According to the Longman Dictionary the hypothesis is “an idea that can be tested to see if it is true or not”. The Urban Dictionary states a hypothesis is “a prediction or educated-guess based on current evidence that is yet be tested”. We argue that the hypotheses of exploratory research— working hypothesis — are not bound by relational expectations. It is this flexibility that distinguishes the working hypothesis.

Sutton and Staw (1995) maintain that hypotheses “serve as crucial bridges between theory and data, making explicit how the variables and relationships that follow from a logical argument will be operationalized” (p. 376, italics added). The highly rated journal, Computers and Education , Twining et al. ( 2017 ) created guidelines for qualitative research as a way to improve soundness and rigor. They identified the lack of alignment between theoretical stance and methodology as a common problem in qualitative research. In addition, they identified a lack of alignment between methodology, design, instruments of data collection and analysis. The authors created a guidance summary, which emphasized the need to enhance coherence throughout elements of research design (Twining et al. 2017 p. 12). Perhaps the bridging function of the hypothesis mentioned by Sutton and Staw (1995) is obscured and often missing in qualitative methods. Working hypotheses can be a tool to overcome this problem.

For reasons, similar to those used by mixed methods scholars, we look to classical pragmatism and the ideas of John Dewey to inform our discussion of theory and working hypotheses. Dewey ( 1938 ) treats theory as a tool of empirical inquiry and uses a map metaphor (p. 136). Theory is like a map that helps a traveler navigate the terrain—and should be judged by its usefulness. “There is no expectation that a map is a true representation of reality. Rather, it is a representation that allows a traveler to reach a destination (achieve a purpose). Hence, theories should be judged by how well they help resolve the problem or achieve a purpose ” (Shields and Rangarajan 2013 , p. 23). Note that we explicitly link theory to the research purpose. Theory is never treated as an unimpeachable Truth, rather it is a helpful tool that organizes inquiry connecting data and problem. Dewey’s approach also expands the definition of theory to include abstractions (categories) outside of causation and explanation. The micro-conceptual frameworks 7 introduced in Table  1 are a type of theory. We define conceptual frameworks as the “way the ideas are organized to achieve the project’s purpose” (Shields and Rangarajan 2013 p. 24). Micro-conceptual frameworks do this at the very close to the data level of analysis. Micro-conceptual frameworks can direct operationalization and ways to assess measurement or evidence at the individual research study level. Again, the research purpose plays a pivotal role in the functioning of theory (Shields and Tajalli 2006 ).

Working hypothesis: methods and data analysis

We move on to answer the remaining questions in the Table  1 . We have established that exploratory research is extremely flexible and idiosyncratic. Given this, we will proceed with a few examples and draw out lessons for developing an exploratory purpose, building a framework and from there identifying data collection techniques and the logics of hypotheses testing and analysis. Early on we noted the value of the Working Hypothesis framework for student empirical research and applied research. The next section uses a masters level student’s work to illustrate the usefulness of working hypotheses as a way to incorporate the literature and structure inquiry. This graduate student was also a mature professional with a research question that emerged from his job and is thus an example of applied research.

Master of Public Administration student, Swift ( 2010 ) worked for a public agency and was responsible for that agency’s sexual harassment training. The agency needed to evaluate its training but had never done so before. He also had never attempted a significant empirical research project. Both of these conditions suggest exploration as a possible approach. He was interested in evaluating the training program and hence the project had a normative sense. Given his job, he already knew a lot about the problem of sexual harassment and sexual harassment training. What he did not know much about was doing empirical research, reviewing the literature or building a framework to evaluate the training (working hypotheses). He wanted a framework that was flexible and comprehensive. In his research, he discovered Lundvall’s ( 2006 ) knowledge taxonomy summarized with four simple ways of knowing ( Know - what, Know - how, Know - why, Know - who ). He asked whether his agency’s training provided the participants with these kinds of knowledge? Lundvall’s categories of knowing became the basis of his working hypotheses. Lundvall’s knowledge taxonomy is well suited for working hypotheses because it is so simple and is easy to understand intuitively. It can also be tailored to the unique problematic situation of the researcher. Swift ( 2010 , pp. 38–39) developed four basic working hypotheses:

• WH1: Capital Metro provides adequate know - what knowledge in its sexual harassment training
• WH2: Capital Metro provides adequate know - how knowledge in its sexual harassment training
• WH3: Capital Metro provides adequate know - why knowledge in its sexual harassment training
• WH4: Capital Metro provides adequate know - who knowledge in its sexual harassment training

From here he needed to determine what would determine the different kinds of knowledge. For example, what constitutes “know what” knowledge for sexual harassment training. This is where his knowledge and experience working in the field as well as the literature come into play. According to Lundvall et al. ( 1988 , p. 12) “know what” knowledge is about facts and raw information. Swift ( 2010 ) learned through the literature that laws and rules were the basis for the mandated sexual harassment training. He read about specific anti-discrimination laws and the subsequent rules and regulations derived from the laws. These laws and rules used specific definitions and were enacted within a historical context. Laws, rules, definitions and history became the “facts” of Know-What knowledge for his working hypothesis. To make this clear, he created sub-hypotheses that explicitly took these into account. See how Swift ( 2010 , p. 38) constructed the sub-hypotheses below. Each sub-hypothesis was defended using material from the literature (Swift 2010 , pp. 22–26). The sub-hypotheses can also be easily tied to evidence. For example, he could document that the training covered anti-discrimination laws.

WH1: Capital Metro provides adequate know - what knowledge in its sexual Harassment training

• WH1a: The sexual harassment training includes information on anti-discrimination laws (Title VII).
• WH1b: The sexual harassment training includes information on key definitions.
• WH1c: The sexual harassment training includes information on Capital Metro’s Equal Employment Opportunity and Harassment policy.
• WH1d: Capital Metro provides training on sexual harassment history.

Know-How knowledge refers to the ability to do something and involves skills (Lundvall and Johnson 1994 , p. 12). It is a kind of expertise in action. The literature and his experience allowed James Smith to identify skills such as how to file a claim or how to document incidents of sexual harassment as important “know-how” knowledge that should be included in sexual harassment training. Again, these were depicted as sub-hypotheses.

WH2: Capital Metro provides adequate know - how knowledge in its sexual Harassment training

• WH2a: Training is provided on how to file and report a claim of harassment
• WH2b: Training is provided on how to document sexual harassment situations.
• WH2c: Training is provided on how to investigate sexual harassment complaints.
• WH2d: Training is provided on how to follow additional harassment policy procedures protocol

Note that the working hypotheses do not specify a relationship but rather are simple declarative sentences. If “know-how” knowledge was found in the sexual harassment training, he would be able to find evidence that participants learned about how to file a claim (WH2a). The working hypothesis provides the bridge between theory and data that Sutton and Staw (1995) found missing in exploratory work. The sub-hypotheses are designed to be refined enough that the researchers would know what to look for and tailor their hunt for evidence. Figure  1 captures the generic sub-hypothesis design.

A Common structure used in the development of working hypotheses

When expected evidence is linked to the sub-hypotheses, data, framework and research purpose are aligned. This can be laid out in a planning document that operationalizes the data collection in something akin to an architect’s blueprint. This is where the scholar explicitly develops the alignment between purpose, framework and method (Shields and Rangarajan 2013 ; Shields et al. 2019b ).

Table  2 operationalizes Swift’s working hypotheses (and sub-hypotheses). The table provide clues as to what kind of evidence is needed to determine whether the hypotheses are supported. In this case, Smith used interviews with participants and trainers as well as a review of program documents. Column one repeats the sub-hypothesis, column two specifies the data collection method (here interviews with participants/managers and review of program documents) and column three specifies the unique questions that focus the investigation. For example, the interview questions are provided. In the less precise world of qualitative data, evidence supporting a hypothesis could have varying degrees of strength. This too can be specified.

Table 2

Operationalization of the working hypotheses: an example

For Swift’s example, neither the statistics of explanatory research nor the open-ended questions of interpretivist, inductive exploratory research is used. The deductive logic of inquiry here is somewhat intuitive and similar to a detective (Ulriksen and Dadalauri 2016 ). It is also a logic used in international law (Worster 2013 ). It should be noted that the working hypothesis and the corresponding data collection protocol does not stop inquiry and fieldwork outside the framework. The interviews could reveal an unexpected problem with Smith’s training program. The framework provides a very loose and perhaps useful ways to identify and make sense of the data that does not fit the expectations. Researchers using working hypotheses should be sensitive to interesting findings that fall outside their framework. These could be used in future studies, to refine theory or even in this case provide suggestions to improve sexual harassment training. The sensitizing concepts mentioned by Gilgun ( 2015 ) are free to emerge and should be encouraged.

Something akin to working hypotheses are hidden in plain sight in the professional literature. Take for example Kerry Crawford’s ( 2017 ) book Wartime Sexual Violence. Here she explores how basic changes in the way “advocates and decision makers think about and discuss conflict-related sexual violence” (p. 2). She focused on a subsequent shift from silence to action. The shift occurred as wartime sexual violence was reframed as a “weapon of war”. The new frame captured the attention of powerful members of the security community who demanded, initiated, and paid for institutional and policy change. Crawford ( 2017 ) examines the legacy of this key reframing. She develops a six-stage model of potential international responses to incidents of wartime violence. This model is fairly easily converted to working hypotheses and sub-hypotheses. Table  3 shows her model as a set of (non-relational) working hypotheses. She applied this model as a way to gather evidence among cases (e.g., the US response to sexual violence in the Democratic Republic of the Congo) to show the official level of response to sexual violence. Each case study chapter examined evidence to establish whether the case fit the pattern formalized in the working hypotheses. The framework was very useful in her comparative context. The framework allowed for consistent comparative analysis across cases. Her analysis of the three cases went well beyond the material covered in the framework. She freely incorporated useful inductively informed data in her analysis and discussion. The framework, however, allowed for alignment within and across cases.

Table 3

Example illustrating a set of working hypotheses as a framework for comparative case studies

Source : Adaptation from Table 1.1 of Crawford’s ( 2017 ) book Wartime Sexual Violence

In this article we argued that the exploratory research is also well suited for deductive approaches. By examining the landscape of deductive, exploratory research, we proposed the working hypothesis as a flexible conceptual framework and a useful tool for doing exploratory studies. It has the potential to guide and bring coherence across the steps in the research process. After presenting the nature of exploratory research purpose and how it differs from two types of research purposes identified in the literature—explanation, and description. We focused on answering four different questions in order to show the link between micro-conceptual frameworks and research purposes in a deductive setting. The answers to the four questions are summarized in Table  4 .

Table 4

Linking micro-conceptual frameworks and research purposes in deductive research

Firstly, we argued that working hypothesis and exploration are situated within the pragmatic philosophical perspective. Pragmatism allows for pluralism in theory and data collection techniques, which is compatible with the flexible exploratory purpose. Secondly, after introducing and discussing the four core elements of pragmatism (practical, pluralism, participatory, and provisional), we explained how the working hypothesis informs the methodologies and evidence collection of deductive exploratory research through a presentation of the benefits of triangulation provided by mixed methods research. Thirdly, as is clear from the article title, we introduced the working hypothesis as the micro-conceptual framework for deductive explorative research. We argued that the hypotheses of explorative research, which we call working hypotheses are distinguished from those of the explanatory research, since they do not require a relational component and are not bound by relational expectations. A working hypothesis is extremely flexible and idiosyncratic, and it could be viewed as a statement or group of statements of expectations tested in action depending on the research question. Using examples, we concluded by explaining how working hypotheses inform data collection and analysis for deductive exploratory research.

Crawford’s ( 2017 ) example showed how the structure of working hypotheses provide a framework for comparative case studies. Her criteria for analysis were specified ahead of time and used to frame each case. Thus, her comparisons were systemized across cases. Further, the framework ensured a connection between the data analysis and the literature review. Yet the flexible, working nature of the hypotheses allowed for unexpected findings to be discovered.

The evidence required to test working hypotheses is directed by the research purpose and potentially includes both quantitative and qualitative sources. Thus, all types of evidence, including quantitative methods should be part of the toolbox of deductive, explorative research. We show how the working hypotheses, as a flexible exploratory framework, resolves many seeming dualisms pervasive in the research methods literature.

To conclude, this article has provided an in-depth examination of working hypotheses taking into account philosophical questions and the larger formal research methods literature. By discussing working hypotheses as applied, theoretical tools, we demonstrated that working hypotheses fill a unique niche in the methods literature, since they provide a way to enhance alignment in deductive, explorative studies.

Acknowledgements

The authors contributed equally to this work. The authors would like to thank Quality & Quantity’ s editors and the anonymous reviewers for their valuable advice and comments on previous versions of this paper.

Open access funding provided by Alma Mater Studiorum - Università di Bologna within the CRUI-CARE Agreement. There are no funders to report for this submission.

Compliance with ethical standards

No potential conflict of interest was reported by the author.

1 In practice, quantitative scholars often run multivariate analysis on data bases to find out if there are correlations. Hypotheses are tested because the statistical software does the math, not because the scholar has an a priori, relational expectation (hypothesis) well-grounded in the literature and supported by cogent arguments. Hunches are just fine. This is clearly an inductive approach to research and part of the large process of inquiry.

2 In 1958 , Philosophers of Science, Oppenheim and Putnam use the notion of Working Hypothesis in their title “Unity of Science as Working Hypothesis.” They too, use it as a big picture concept, “unity of science in this sense, can be fully realized constitutes an over-arching meta-scientific hypothesis, which enables one to see a unity in scientific activities that might otherwise appear disconnected or unrelated” (p. 4).

3 It should be noted that the positivism described in the research methods literature does not resemble philosophical positivism as developed by philosophers like Comte (Whetsell and Shields 2015 ). In the research methods literature “positivism means different things to different people….The term has long been emptied of any precise denotation …and is sometimes affixed to positions actually opposed to those espoused by the philosophers from whom the name derives” (Schrag 1992 , p. 5). For purposes of this paper, we are capturing a few essential ways positivism is presented in the research methods literature. This helps us to position the “working hypothesis” and “exploratory” research within the larger context in contemporary research methods. We are not arguing that the positivism presented here is anything more. The incompatibility theory discussed later, is an outgrowth of this research methods literature…

4 It should be noted that quantitative researchers often use inductive reasoning. They do this with existing data sets when they run correlations or regression analysis as a way to find relationships. They ask, what does the data tell us?

5 Qualitative researchers are also associated with phenomenology, hermeneutics, naturalistic inquiry and constructivism.

6 See Feilzer ( 2010 ), Howe ( 1988 ), Johnson and Onwuegbunzie ( 2004 ), Morgan ( 2007 ), Onwuegbuzie and Leech ( 2005 ), Biddle and Schafft ( 2015 ).

7 The term conceptual framework is applicable in a broad context (see Ravitch and Riggan 2012 ). The micro-conceptual framework narrows to the specific study and informs data collection (Shields and Rangarajan 2013 ; Shields et al. 2019a ) .

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Contributor Information

Mattia Casula, Email: [email protected] .

Nandhini Rangarajan, Email: ude.etatsxt@11rn .

Patricia Shields, Email: ude.etatsxt@70sp .

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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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The potential of working hypotheses for deductive exploratory research

• Open access
• Published: 08 December 2020
• Volume 55 , pages 1703–1725, ( 2021 )

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• Mattia Casula   ORCID: orcid.org/0000-0002-7081-8153 1 ,
• Nandhini Rangarajan 2 &
• Patricia Shields   ORCID: orcid.org/0000-0002-0960-4869 2  

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While hypotheses frame explanatory studies and provide guidance for measurement and statistical tests, deductive, exploratory research does not have a framing device like the hypothesis. To this purpose, this article examines the landscape of deductive, exploratory research and offers the working hypothesis as a flexible, useful framework that can guide and bring coherence across the steps in the research process. The working hypothesis conceptual framework is introduced, placed in a philosophical context, defined, and applied to public administration and comparative public policy. Doing so, this article explains: the philosophical underpinning of exploratory, deductive research; how the working hypothesis informs the methodologies and evidence collection of deductive, explorative research; the nature of micro-conceptual frameworks for deductive exploratory research; and, how the working hypothesis informs data analysis when exploratory research is deductive.

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1 Introduction

Exploratory research is generally considered to be inductive and qualitative (Stebbins 2001 ). Exploratory qualitative studies adopting an inductive approach do not lend themselves to a priori theorizing and building upon prior bodies of knowledge (Reiter 2013 ; Bryman 2004 as cited in Pearse 2019 ). Juxtaposed against quantitative studies that employ deductive confirmatory approaches, exploratory qualitative research is often criticized for lack of methodological rigor and tentativeness in results (Thomas and Magilvy 2011 ). This paper focuses on the neglected topic of deductive, exploratory research and proposes working hypotheses as a useful framework for these studies.

To emphasize that certain types of applied research lend themselves more easily to deductive approaches, to address the downsides of exploratory qualitative research, and to ensure qualitative rigor in exploratory research, a significant body of work on deductive qualitative approaches has emerged (see for example, Gilgun 2005 , 2015 ; Hyde 2000 ; Pearse 2019 ). According to Gilgun ( 2015 , p. 3) the use of conceptual frameworks derived from comprehensive reviews of literature and a priori theorizing were common practices in qualitative research prior to the publication of Glaser and Strauss’s ( 1967 ) The Discovery of Grounded Theory . Gilgun ( 2015 ) coined the terms Deductive Qualitative Analysis (DQA) to arrive at some sort of “middle-ground” such that the benefits of a priori theorizing (structure) and allowing room for new theory to emerge (flexibility) are reaped simultaneously. According to Gilgun ( 2015 , p. 14) “in DQA, the initial conceptual framework and hypotheses are preliminary. The purpose of DQA is to come up with a better theory than researchers had constructed at the outset (Gilgun 2005 , 2009 ). Indeed, the production of new, more useful hypotheses is the goal of DQA”.

DQA provides greater level of structure for both the experienced and novice qualitative researcher (see for example Pearse 2019 ; Gilgun 2005 ). According to Gilgun ( 2015 , p. 4) “conceptual frameworks are the sources of hypotheses and sensitizing concepts”. Sensitizing concepts frame the exploratory research process and guide the researcher’s data collection and reporting efforts. Pearse ( 2019 ) discusses the usefulness for deductive thematic analysis and pattern matching to help guide DQA in business research. Gilgun ( 2005 ) discusses the usefulness of DQA for family research.

Given these rationales for DQA in exploratory research, the overarching purpose of this paper is to contribute to that growing corpus of work on deductive qualitative research. This paper is specifically aimed at guiding novice researchers and student scholars to the working hypothesis as a useful a priori framing tool. The applicability of the working hypothesis as a tool that provides more structure during the design and implementation phases of exploratory research is discussed in detail. Examples of research projects in public administration that use the working hypothesis as a framing tool for deductive exploratory research are provided.

In the next section, we introduce the three types of research purposes. Second, we examine the nature of the exploratory research purpose. Third, we provide a definition of working hypothesis. Fourth, we explore the philosophical roots of methodology to see where exploratory research fits. Fifth, we connect the discussion to the dominant research approaches (quantitative, qualitative and mixed methods) to see where deductive exploratory research fits. Sixth, we examine the nature of theory and the role of the hypothesis in theory. We contrast formal hypotheses and working hypotheses. Seven, we provide examples of student and scholarly work that illustrates how working hypotheses are developed and operationalized. Lastly, this paper synthesizes previous discussion with concluding remarks.

2 Three types of research purposes

The literature identifies three basic types of research purposes—explanation, description and exploration (Babbie 2007 ; Adler and Clark 2008 ; Strydom 2013 ; Shields and Whetsell 2017 ). Research purposes are similar to research questions; however, they focus on project goals or aims instead of questions.

Explanatory research answers the “why” question (Babbie 2007 , pp. 89–90), by explaining “why things are the way they are”, and by looking “for causes and reasons” (Adler and Clark 2008 , p. 14). Explanatory research is closely tied to hypothesis testing. Theory is tested using deductive reasoning, which goes from the general to the specific (Hyde 2000 , p. 83). Hypotheses provide a frame for explanatory research connecting the research purpose to other parts of the research process (variable construction, choice of data, statistical tests). They help provide alignment or coherence across stages in the research process and provide ways to critique the strengths and weakness of the study. For example, were the hypotheses grounded in the appropriate arguments and evidence in the literature? Are the concepts imbedded in the hypotheses appropriately measured? Was the best statistical test used? When the analysis is complete (hypothesis is tested), the results generally answer the research question (the evidence supported or failed to support the hypothesis) (Shields and Rangarajan 2013 ).

Descriptive research addresses the “What” question and is not primarily concerned with causes (Strydom 2013 ; Shields and Tajalli 2006 ). It lies at the “midpoint of the knowledge continuum” (Grinnell 2001 , p. 248) between exploration and explanation. Descriptive research is used in both quantitative and qualitative research. A field researcher might want to “have a more highly developed idea of social phenomena” (Strydom 2013 , p. 154) and develop thick descriptions using inductive logic. In science, categorization and classification systems such as the periodic table of chemistry or the taxonomies of biology inform descriptive research. These baseline classification systems are a type of theorizing and allow researchers to answer questions like “what kind” of plants and animals inhabit a forest. The answer to this question would usually be displayed in graphs and frequency distributions. This is also the data presentation system used in the social sciences (Ritchie and Lewis 2003 ; Strydom 2013 ). For example, if a scholar asked, what are the needs of homeless people? A quantitative approach would include a survey that incorporated a “needs” classification system (preferably based on a literature review). The data would be displayed as frequency distributions or as charts. Description can also be guided by inductive reasoning, which draws “inferences from specific observable phenomena to general rules or knowledge expansion” (Worster 2013 , p. 448). Theory and hypotheses are generated using inductive reasoning, which begins with data and the intention of making sense of it by theorizing. Inductive descriptive approaches would use a qualitative, naturalistic design (open ended interview questions with the homeless population). The data could provide a thick description of the homeless context. For deductive descriptive research, categories, serve a purpose similar to hypotheses for explanatory research. If developed with thought and a connection to the literature, categories can serve as a framework that inform measurement, link to data collection mechanisms and to data analysis. Like hypotheses they can provide horizontal coherence across the steps in the research process.

Table  1 demonstrated these connections for deductive, descriptive and explanatory research. The arrow at the top emphasizes the horizontal or across the research process view we emphasize. This article makes the case that the working hypothesis can serve the same purpose as the hypothesis for deductive, explanatory research and categories for deductive descriptive research. The cells for exploratory research are filled in with question marks.

The remainder of this paper focuses on exploratory research and the answers to questions found in the table:

What is the philosophical underpinning of exploratory, deductive research?

What is the Micro-conceptual framework for deductive exploratory research? [ As is clear from the article title we introduce the working hypothesis as the answer .]

How does the working hypothesis inform the methodologies and evidence collection of deductive exploratory research?

How does the working hypothesis inform data analysis of deductive exploratory research?

3 The nature of exploratory research purpose

Explorers enter the unknown to discover something new. The process can be fraught with struggle and surprises. Effective explorers creatively resolve unexpected problems. While we typically think of explorers as pioneers or mountain climbers, exploration is very much linked to the experience and intention of the explorer. Babies explore as they take their first steps. The exploratory purpose resonates with these insights. Exploratory research, like reconnaissance, is a type of inquiry that is in the preliminary or early stages (Babbie 2007 ). It is associated with discovery, creativity and serendipity (Stebbins 2001 ). But the person doing the discovery, also defines the activity or claims the act of exploration. It “typically occurs when a researcher examines a new interest or when the subject of study itself is relatively new” (Babbie 2007 , p. 88). Hence, exploration has an open character that emphasizes “flexibility, pragmatism, and the particular, biographically specific interests of an investigator” (Maanen et al. 2001 , p. v). These three purposes form a type of hierarchy. An area of inquiry is initially explored . This early work lays the ground for, description which in turn becomes the basis for explanation . Quantitative, explanatory studies dominate contemporary high impact journals (Twining et al. 2017 ).

Stebbins ( 2001 ) makes the point that exploration is often seen as something like a poor stepsister to confirmatory or hypothesis testing research. He has a problem with this because we live in a changing world and what is settled today will very likely be unsettled in the near future and in need of exploration. Further, exploratory research “generates initial insights into the nature of an issue and develops questions to be investigated by more extensive studies” (Marlow 2005 , p. 334). Exploration is widely applicable because all research topics were once “new.” Further, all research topics have the possibility of “innovation” or ongoing “newness”. Exploratory research may be appropriate to establish whether a phenomenon exists (Strydom 2013 ). The point here, of course, is that the exploratory purpose is far from trivial.

Stebbins’ Exploratory Research in the Social Sciences ( 2001 ), is the only book devoted to the nature of exploratory research as a form of social science inquiry. He views it as a “broad-ranging, purposive, systematic prearranged undertaking designed to maximize the discovery of generalizations leading to description and understanding of an area of social or psychological life” (p. 3). It is science conducted in a way distinct from confirmation. According to Stebbins ( 2001 , p. 6) the goal is discovery of potential generalizations, which can become future hypotheses and eventually theories that emerge from the data. He focuses on inductive logic (which stimulates creativity) and qualitative methods. He does not want exploratory research limited to the restrictive formulas and models he finds in confirmatory research. He links exploratory research to Glaser and Strauss’s ( 1967 ) flexible, immersive, Grounded Theory. Strydom’s ( 2013 ) analysis of contemporary social work research methods books echoes Stebbins’ ( 2001 ) position. Stebbins’s book is an important contribution, but it limits the potential scope of this flexible and versatile research purpose. If we accepted his conclusion, we would delete the “Exploratory” row from Table  1 .

Note that explanatory research can yield new questions, which lead to exploration. Inquiry is a process where inductive and deductive activities can occur simultaneously or in a back and forth manner, particularly as the literature is reviewed and the research design emerges. Footnote 1 Strict typologies such as explanation, description and exploration or inductive/deductive can obscures these larger connections and processes. We draw insight from Dewey’s ( 1896 ) vision of inquiry as depicted in his seminal “Reflex Arc” article. He notes that “stimulus” and “response” like other dualities (inductive/deductive) exist within a larger unifying system. Yet the terms have value. “We need not abandon terms like stimulus and response, so long as we remember that they are attached to events based upon their function in a wider dynamic context, one that includes interests and aims” (Hildebrand 2008 , p. 16). So too, in methodology typologies such as deductive/inductive capture useful distinctions with practical value and are widely used in the methodology literature.

We argue that there is a role for exploratory, deductive, and confirmatory research. We maintain all types of research logics and methods should be in the toolbox of exploratory research. First, as stated above, it makes no sense on its face to identify an extremely flexible purpose that is idiosyncratic to the researcher and then basically restrict its use to qualitative, inductive, non-confirmatory methods. Second, Stebbins’s ( 2001 ) work focused on social science ignoring the policy sciences. Exploratory research can be ideal for immediate practical problems faced by policy makers, who could find a framework of some kind useful. Third, deductive, exploratory research is more intentionally connected to previous research. Some kind of initial framing device is located or designed using the literature. This may be very important for new scholars who are developing research skills and exploring their field and profession. Stebbins’s insights are most pertinent for experienced scholars. Fourth, frameworks and deductive logic are useful for comparative work because some degree of consistency across cases is built into the design.

As we have seen, the hypotheses of explanatory and categories of descriptive research are the dominate frames of social science and policy science. We certainly concur that neither of these frames makes a lot of sense for exploratory research. They would tend to tie it down. We see the problem as a missing framework or missing way to frame deductive, exploratory research in the methodology literature. Inductive exploratory research would not work for many case studies that are trying to use evidence to make an argument. What exploratory deductive case studies need is a framework that incorporates flexibility. This is even more true for comparative case studies. A framework of this sort could be usefully applied to policy research (Casula 2020a ), particularly evaluative policy research, and applied research generally. We propose the Working Hypothesis as a flexible conceptual framework and as a useful tool for doing exploratory studies. It can be used as an evaluative criterion particularly for process evaluation and is useful for student research because students can develop theorizing skills using the literature.

Table  1 included a column specifying the philosophical basis for each research purpose. Shifting gears to the philosophical underpinning of methodology provides useful additional context for examination of deductive, exploratory research.

4 What is a working hypothesis

The working hypothesis is first and foremost a hypothesis or a statement of expectation that is tested in action. The term “working” suggest that these hypotheses are subject to change, are provisional and the possibility of finding contradictory evidence is real. In addition, a “working” hypothesis is active, it is a tool in an ongoing process of inquiry. If one begins with a research question, the working hypothesis could be viewed as a statement or group of statements that answer the question. It “works” to move purposeful inquiry forward. “Working” also implies some sort of community, mostly we work together in relationship to achieve some goal.

Working Hypothesis is a term found in earlier literature. Indeed, both pioneering pragmatists, John Dewey and George Herbert Mead use the term working hypothesis in important nineteenth century works. For both Dewey and Mead, the notion of a working hypothesis has a self-evident quality and it is applied in a big picture context. Footnote 2

Most notably, Dewey ( 1896 ), in one of his most pivotal early works (“Reflex Arc”), used “working hypothesis” to describe a key concept in psychology. “The idea of the reflex arc has upon the whole come nearer to meeting this demand for a general working hypothesis than any other single concept (Italics added)” (p. 357). The notion of a working hypothesis was developed more fully 42 years later, in Logic the Theory of Inquiry , where Dewey developed the notion of a working hypothesis that operated on a smaller scale. He defines working hypotheses as a “provisional, working means of advancing investigation” (Dewey 1938 , pp. 142). Dewey’s definition suggests that working hypotheses would be useful toward the beginning of a research project (e.g., exploratory research).

Mead ( 1899 ) used working hypothesis in a title of an American Journal of Sociology article “The Working Hypothesis and Social Reform” (italics added). He notes that a scientist’s foresight goes beyond testing a hypothesis.

Given its success, he may restate his world from this standpoint and get the basis for further investigation that again always takes the form of a problem. The solution of this problem is found over again in the possibility of fitting his hypothetical proposition into the whole within which it arises. And he must recognize that this statement is only a working hypothesis at the best, i.e., he knows that further investigation will show that the former statement of his world is only provisionally true, and must be false from the standpoint of a larger knowledge, as every partial truth is necessarily false over against the fuller knowledge which he will gain later (Mead 1899 , p. 370).

Cronbach ( 1975 ) developed a notion of working hypothesis consistent with inductive reasoning, but for him, the working hypothesis is a product or result of naturalistic inquiry. He makes the case that naturalistic inquiry is highly context dependent and therefore results or seeming generalizations that may come from a study and should be viewed as “working hypotheses”, which “are tentative both for the situation in which they first uncovered and for other situations” (as cited in Gobo 2008 , p. 196).

A quick Google scholar search using the term “working hypothesis” show that it is widely used in twentieth and twenty-first century science, particularly in titles. In these articles, the working hypothesis is treated as a conceptual tool that furthers investigation in its early or transitioning phases. We could find no explicit links to exploratory research. The exploratory nature of the problem is expressed implicitly. Terms such as “speculative” (Habib 2000 , p. 2391) or “rapidly evolving field” (Prater et al. 2007 , p. 1141) capture the exploratory nature of the study. The authors might describe how a topic is “new” or reference “change”. “As a working hypothesis, the picture is only new, however, in its interpretation” (Milnes 1974 , p. 1731). In a study of soil genesis, Arnold ( 1965 , p. 718) notes “Sequential models, formulated as working hypotheses, are subject to further investigation and change”. Any 2020 article dealing with COVID-19 and respiratory distress would be preliminary almost by definition (Ciceri et al. 2020 ).

5 Philosophical roots of methodology

According to Kaplan ( 1964 , p. 23) “the aim of methodology is to help us understand, in the broadest sense not the products of scientific inquiry but the process itself”. Methods contain philosophical principles that distinguish them from other “human enterprises and interests” (Kaplan 1964 , p. 23). Contemporary research methodology is generally classified as quantitative, qualitative and mixed methods. Leading scholars of methodology have associated each with a philosophical underpinning—positivism (or post-positivism), interpretivism or constructivist and pragmatism, respectively (Guba 1987 ; Guba and Lincoln 1981 ; Schrag 1992 ; Stebbins 2001 ; Mackenzi and Knipe 2006 ; Atieno 2009 ; Levers 2013 ; Morgan 2007 ; O’Connor et al. 2008 ; Johnson and Onwuegbuzie 2004 ; Twining et al. 2017 ). This section summarizes how the literature often describes these philosophies and informs contemporary methodology and its literature.

Positivism and its more contemporary version, post-positivism, maintains an objectivist ontology or assumes an objective reality, which can be uncovered (Levers 2013 ; Twining et al. 2017 ). Footnote 3 Time and context free generalizations are possible and “real causes of social scientific outcomes can be determined reliably and validly (Johnson and Onwuegbunzie 2004 , p. 14). Further, “explanation of the social world is possible through a logical reduction of social phenomena to physical terms”. It uses an empiricist epistemology which “implies testability against observation, experimentation, or comparison” (Whetsell and Shields 2015 , pp. 420–421). Correspondence theory, a tenet of positivism, asserts that “to each concept there corresponds a set of operations involved in its scientific use” (Kaplan 1964 , p. 40).

The interpretivist, constructivists or post-modernist approach is a reaction to positivism. It uses a relativist ontology and a subjectivist epistemology (Levers 2013 ). In this world of multiple realities, context free generalities are impossible as is the separation of facts and values. Causality, explanation, prediction, experimentation depend on assumptions about the correspondence between concepts and reality, which in the absence of an objective reality is impossible. Empirical research can yield “contextualized emergent understanding rather than the creation of testable theoretical structures” (O’Connor et al. 2008 , p. 30). The distinctively different world views of positivist/post positivist and interpretivist philosophy is at the core of many controversies in methodology, social and policy science literature (Casula 2020b ).

With its focus on dissolving dualisms, pragmatism steps outside the objective/subjective debate. Instead, it asks, “what difference would it make to us if the statement were true” (Kaplan 1964 , p. 42). Its epistemology is connected to purposeful inquiry. Pragmatism has a “transformative, experimental notion of inquiry” anchored in pluralism and a focus on constructing conceptual and practical tools to resolve “problematic situations” (Shields 1998 ; Shields and Rangarajan 2013 ). Exploration and working hypotheses are most comfortably situated within the pragmatic philosophical perspective.

6 Research approaches

Empirical investigation relies on three types of methodology—quantitative, qualitative and mixed methods.

6.1 Quantitative methods

Quantitative methods uses deductive logic and formal hypotheses or models to explain, predict, and eventually establish causation (Hyde 2000 ; Kaplan 1964 ; Johnson and Onwuegbunzie 2004 ; Morgan 2007 ). Footnote 4 The correspondence between the conceptual and empirical world make measures possible. Measurement assigns numbers to objects, events or situations and allows for standardization and subtle discrimination. It also allows researchers to draw on the power of mathematics and statistics (Kaplan 1964 , pp. 172–174). Using the power of inferential statistics, quantitative research employs research designs, which eliminate competing hypotheses. It is high in external validity or the ability to generalize to the whole. The research results are relatively independent of the researcher (Johnson & Onwuegbunzie 2004 ).

Quantitative methods depend on the quality of measurement and a priori conceptualization, and adherence to the underlying assumptions of inferential statistics. Critics charge that hypotheses and frameworks needlessly constrain inquiry (Johnson and Onwuegbunzie 2004 , p. 19). Hypothesis testing quantitative methods support the explanatory purpose.

6.2 Qualitative methods

Qualitative researchers who embrace the post-modern, interpretivist view, Footnote 5 question everything about the nature of quantitative methods (Willis et al. 2007 ). Rejecting the possibility of objectivity, correspondence between ideas and measures, and the constraints of a priori theorizing they focus on “unique impressions and understandings of events rather than to generalize the findings” (Kolb 2012 , p. 85). Characteristics of traditional qualitative research include “induction, discovery, exploration, theory/hypothesis generation and the researcher as the primary ‘instrument’ of data collection” (Johnson and Onwuegbunzie 2004 , p. 18). It also concerns itself with forming “unique impressions and understandings of events rather than to generalize findings” (Kolb 2012 , p. 85). The data of qualitative methods are generated via interviews, direct observation, focus groups and analysis of written records or artifacts.

Qualitative methods provide for understanding and “description of people’s personal experiences of phenomena”. They enable descriptions of detailed “phenomena as they are situated and embedded in local contexts.” Researchers use naturalistic settings to “study dynamic processes” and explore how participants interpret experiences. Qualitative methods have an inherent flexibility, allowing researchers to respond to changes in the research setting. They are particularly good at narrowing to the particular and on the flipside have limited external validity (Johnson and Onwuegbunzie 2004 , p. 20). Instead of specifying a suitable sample size to draw conclusions, qualitative research uses the notion of saturation (Morse 1995 ).

Saturation is used in grounded theory—a widely used and respected form of qualitative research, and a well-known interpretivist qualitative research method. Introduced by Glaser and Strauss ( 1967 ), this “grounded on observation” (Patten and Newhart 2000 , p. 27) methodology, focuses on “the creation of emergent understanding” (O’Connor et al. 2008 , p. 30). It uses the Constant Comparative method, whereby researchers develop theory from data as they code and analyze at the same time. Data collection, coding and analysis along with theoretical sampling are systematically combined to generate theory (Kolb 2012 , p. 83). The qualitative methods discussed here support exploratory research.

A close look at the two philosophies and assumptions of quantitative and qualitative research suggests two contradictory world views. The literature has labeled these contradictory views the Incompatibility Theory, which sets up a quantitative versus qualitative tension similar to the seeming separation of art and science or fact and values (Smith 1983a , b ; Guba 1987 ; Smith and Heshusius 1986 ; Howe 1988 ). The incompatibility theory does not make sense in practice. Yin ( 1981 , 1992 , 2011 , 2017 ), a prominent case study scholar, showcases a deductive research methodology that crosses boundaries using both quantaitive and qualitative evidence when appropriate.

6.3 Mixed methods

Turning the “Incompatibility Theory” on its head, Mixed Methods research “combines elements of qualitative and quantitative research approaches … for the broad purposes of breadth and depth of understanding and corroboration” (Johnson et al. 2007 , p. 123). It does this by partnering with philosophical pragmatism. Footnote 6 Pragmatism is productive because “it offers an immediate and useful middle position philosophically and methodologically; it offers a practical and outcome-oriented method of inquiry that is based on action and leads, iteratively, to further action and the elimination of doubt; it offers a method for selecting methodological mixes that can help researchers better answer many of their research questions” (Johnson and Onwuegbunzie 2004 , p. 17). What is theory for the pragmatist “any theoretical model is for the pragmatist, nothing more than a framework through which problems are perceived and subsequently organized ” (Hothersall 2019 , p. 5).

Brendel ( 2009 ) constructed a simple framework to capture the core elements of pragmatism. Brendel’s four “p”’s—practical, pluralism, participatory and provisional help to show the relevance of pragmatism to mixed methods. Pragmatism is purposeful and concerned with the practical consequences. The pluralism of pragmatism overcomes quantitative/qualitative dualism. Instead, it allows for multiple perspectives (including positivism and interpretivism) and, thus, gets around the incompatibility problem. Inquiry should be participatory or inclusive of the many views of participants, hence, it is consistent with multiple realities and is also tied to the common concern of a problematic situation. Finally, all inquiry is provisional . This is compatible with experimental methods, hypothesis testing and consistent with the back and forth of inductive and deductive reasoning. Mixed methods support exploratory research.

Advocates of mixed methods research note that it overcomes the weaknesses and employs the strengths of quantitative and qualitative methods. Quantitative methods provide precision. The pictures and narrative of qualitative techniques add meaning to the numbers. Quantitative analysis can provide a big picture, establish relationships and its results have great generalizability. On the other hand, the “why” behind the explanation is often missing and can be filled in through in-depth interviews. A deeper and more satisfying explanation is possible. Mixed-methods brings the benefits of triangulation or multiple sources of evidence that converge to support a conclusion. It can entertain a “broader and more complete range of research questions” (Johnson and Onwuegbunzie 2004 , p. 21) and can move between inductive and deductive methods. Case studies use multiple forms of evidence and are a natural context for mixed methods.

One thing that seems to be missing from mixed method literature and explicit design is a place for conceptual frameworks. For example, Heyvaert et al. ( 2013 ) examined nine mixed methods studies and found an explicit framework in only two studies (transformative and pragmatic) (p. 663).

7 Theory and hypotheses: where is and what is theory?

Theory is key to deductive research. In essence, empirical deductive methods test theory. Hence, we shift our attention to theory and the role and functions of the hypotheses in theory. Oppenheim and Putnam ( 1958 ) note that “by a ‘theory’ (in the widest sense) we mean any hypothesis, generalization or law (whether deterministic or statistical) or any conjunction of these” (p. 25). Van Evera ( 1997 ) uses a similar and more complex definition “theories are general statements that describe and explain the causes of effects of classes of phenomena. They are composed of causal laws or hypotheses, explanations, and antecedent conditions” (p. 8). Sutton and Staw ( 1995 , p. 376) in a highly cited article “What Theory is Not” assert the that hypotheses should contain logical arguments for “why” the hypothesis is expected. Hypotheses need an underlying causal argument before they can be considered theory. The point of this discussion is not to define theory but to establish the importance of hypotheses in theory.

Explanatory research is implicitly relational (A explains B). The hypotheses of explanatory research lay bare these relationships. Popular definitions of hypotheses capture this relational component. For example, the Cambridge Dictionary defines a hypothesis a “an idea or explanation for something that is based on known facts but has not yet been proven”. Vocabulary.Com’s definition emphasizes explanation, a hypothesis is “an idea or explanation that you then test through study and experimentation”. According to Wikipedia a hypothesis is “a proposed explanation for a phenomenon”. Other definitions remove the relational or explanatory reference. The Oxford English Dictionary defines a hypothesis as a “supposition or conjecture put forth to account for known facts.” Science Buddies defines a hypothesis as a “tentative, testable answer to a scientific question”. According to the Longman Dictionary the hypothesis is “an idea that can be tested to see if it is true or not”. The Urban Dictionary states a hypothesis is “a prediction or educated-guess based on current evidence that is yet be tested”. We argue that the hypotheses of exploratory research— working hypothesis — are not bound by relational expectations. It is this flexibility that distinguishes the working hypothesis.

Sutton and Staw (1995) maintain that hypotheses “serve as crucial bridges between theory and data, making explicit how the variables and relationships that follow from a logical argument will be operationalized” (p. 376, italics added). The highly rated journal, Computers and Education , Twining et al. ( 2017 ) created guidelines for qualitative research as a way to improve soundness and rigor. They identified the lack of alignment between theoretical stance and methodology as a common problem in qualitative research. In addition, they identified a lack of alignment between methodology, design, instruments of data collection and analysis. The authors created a guidance summary, which emphasized the need to enhance coherence throughout elements of research design (Twining et al. 2017 p. 12). Perhaps the bridging function of the hypothesis mentioned by Sutton and Staw (1995) is obscured and often missing in qualitative methods. Working hypotheses can be a tool to overcome this problem.

For reasons, similar to those used by mixed methods scholars, we look to classical pragmatism and the ideas of John Dewey to inform our discussion of theory and working hypotheses. Dewey ( 1938 ) treats theory as a tool of empirical inquiry and uses a map metaphor (p. 136). Theory is like a map that helps a traveler navigate the terrain—and should be judged by its usefulness. “There is no expectation that a map is a true representation of reality. Rather, it is a representation that allows a traveler to reach a destination (achieve a purpose). Hence, theories should be judged by how well they help resolve the problem or achieve a purpose ” (Shields and Rangarajan 2013 , p. 23). Note that we explicitly link theory to the research purpose. Theory is never treated as an unimpeachable Truth, rather it is a helpful tool that organizes inquiry connecting data and problem. Dewey’s approach also expands the definition of theory to include abstractions (categories) outside of causation and explanation. The micro-conceptual frameworks Footnote 7 introduced in Table  1 are a type of theory. We define conceptual frameworks as the “way the ideas are organized to achieve the project’s purpose” (Shields and Rangarajan 2013 p. 24). Micro-conceptual frameworks do this at the very close to the data level of analysis. Micro-conceptual frameworks can direct operationalization and ways to assess measurement or evidence at the individual research study level. Again, the research purpose plays a pivotal role in the functioning of theory (Shields and Tajalli 2006 ).

8 Working hypothesis: methods and data analysis

We move on to answer the remaining questions in the Table  1 . We have established that exploratory research is extremely flexible and idiosyncratic. Given this, we will proceed with a few examples and draw out lessons for developing an exploratory purpose, building a framework and from there identifying data collection techniques and the logics of hypotheses testing and analysis. Early on we noted the value of the Working Hypothesis framework for student empirical research and applied research. The next section uses a masters level student’s work to illustrate the usefulness of working hypotheses as a way to incorporate the literature and structure inquiry. This graduate student was also a mature professional with a research question that emerged from his job and is thus an example of applied research.

Master of Public Administration student, Swift ( 2010 ) worked for a public agency and was responsible for that agency’s sexual harassment training. The agency needed to evaluate its training but had never done so before. He also had never attempted a significant empirical research project. Both of these conditions suggest exploration as a possible approach. He was interested in evaluating the training program and hence the project had a normative sense. Given his job, he already knew a lot about the problem of sexual harassment and sexual harassment training. What he did not know much about was doing empirical research, reviewing the literature or building a framework to evaluate the training (working hypotheses). He wanted a framework that was flexible and comprehensive. In his research, he discovered Lundvall’s ( 2006 ) knowledge taxonomy summarized with four simple ways of knowing ( Know - what, Know - how, Know - why, Know - who ). He asked whether his agency’s training provided the participants with these kinds of knowledge? Lundvall’s categories of knowing became the basis of his working hypotheses. Lundvall’s knowledge taxonomy is well suited for working hypotheses because it is so simple and is easy to understand intuitively. It can also be tailored to the unique problematic situation of the researcher. Swift ( 2010 , pp. 38–39) developed four basic working hypotheses:

WH1: Capital Metro provides adequate know - what knowledge in its sexual harassment training

WH2: Capital Metro provides adequate know - how knowledge in its sexual harassment training

WH3: Capital Metro provides adequate know - why knowledge in its sexual harassment training

WH4: Capital Metro provides adequate know - who knowledge in its sexual harassment training

From here he needed to determine what would determine the different kinds of knowledge. For example, what constitutes “know what” knowledge for sexual harassment training. This is where his knowledge and experience working in the field as well as the literature come into play. According to Lundvall et al. ( 1988 , p. 12) “know what” knowledge is about facts and raw information. Swift ( 2010 ) learned through the literature that laws and rules were the basis for the mandated sexual harassment training. He read about specific anti-discrimination laws and the subsequent rules and regulations derived from the laws. These laws and rules used specific definitions and were enacted within a historical context. Laws, rules, definitions and history became the “facts” of Know-What knowledge for his working hypothesis. To make this clear, he created sub-hypotheses that explicitly took these into account. See how Swift ( 2010 , p. 38) constructed the sub-hypotheses below. Each sub-hypothesis was defended using material from the literature (Swift 2010 , pp. 22–26). The sub-hypotheses can also be easily tied to evidence. For example, he could document that the training covered anti-discrimination laws.

WH1: Capital Metro provides adequate know - what knowledge in its sexual Harassment training

WH1a: The sexual harassment training includes information on anti-discrimination laws (Title VII).

WH1b: The sexual harassment training includes information on key definitions.

WH1c: The sexual harassment training includes information on Capital Metro’s Equal Employment Opportunity and Harassment policy.

WH1d: Capital Metro provides training on sexual harassment history.

Know-How knowledge refers to the ability to do something and involves skills (Lundvall and Johnson 1994 , p. 12). It is a kind of expertise in action. The literature and his experience allowed James Smith to identify skills such as how to file a claim or how to document incidents of sexual harassment as important “know-how” knowledge that should be included in sexual harassment training. Again, these were depicted as sub-hypotheses.

WH2: Capital Metro provides adequate know - how knowledge in its sexual Harassment training

WH2a: Training is provided on how to file and report a claim of harassment

WH2b: Training is provided on how to document sexual harassment situations.

WH2c: Training is provided on how to investigate sexual harassment complaints.

WH2d: Training is provided on how to follow additional harassment policy procedures protocol

Note that the working hypotheses do not specify a relationship but rather are simple declarative sentences. If “know-how” knowledge was found in the sexual harassment training, he would be able to find evidence that participants learned about how to file a claim (WH2a). The working hypothesis provides the bridge between theory and data that Sutton and Staw (1995) found missing in exploratory work. The sub-hypotheses are designed to be refined enough that the researchers would know what to look for and tailor their hunt for evidence. Figure  1 captures the generic sub-hypothesis design.

A Common structure used in the development of working hypotheses

When expected evidence is linked to the sub-hypotheses, data, framework and research purpose are aligned. This can be laid out in a planning document that operationalizes the data collection in something akin to an architect’s blueprint. This is where the scholar explicitly develops the alignment between purpose, framework and method (Shields and Rangarajan 2013 ; Shields et al. 2019b ).

Table  2 operationalizes Swift’s working hypotheses (and sub-hypotheses). The table provide clues as to what kind of evidence is needed to determine whether the hypotheses are supported. In this case, Smith used interviews with participants and trainers as well as a review of program documents. Column one repeats the sub-hypothesis, column two specifies the data collection method (here interviews with participants/managers and review of program documents) and column three specifies the unique questions that focus the investigation. For example, the interview questions are provided. In the less precise world of qualitative data, evidence supporting a hypothesis could have varying degrees of strength. This too can be specified.

For Swift’s example, neither the statistics of explanatory research nor the open-ended questions of interpretivist, inductive exploratory research is used. The deductive logic of inquiry here is somewhat intuitive and similar to a detective (Ulriksen and Dadalauri 2016 ). It is also a logic used in international law (Worster 2013 ). It should be noted that the working hypothesis and the corresponding data collection protocol does not stop inquiry and fieldwork outside the framework. The interviews could reveal an unexpected problem with Smith’s training program. The framework provides a very loose and perhaps useful ways to identify and make sense of the data that does not fit the expectations. Researchers using working hypotheses should be sensitive to interesting findings that fall outside their framework. These could be used in future studies, to refine theory or even in this case provide suggestions to improve sexual harassment training. The sensitizing concepts mentioned by Gilgun ( 2015 ) are free to emerge and should be encouraged.

Something akin to working hypotheses are hidden in plain sight in the professional literature. Take for example Kerry Crawford’s ( 2017 ) book Wartime Sexual Violence. Here she explores how basic changes in the way “advocates and decision makers think about and discuss conflict-related sexual violence” (p. 2). She focused on a subsequent shift from silence to action. The shift occurred as wartime sexual violence was reframed as a “weapon of war”. The new frame captured the attention of powerful members of the security community who demanded, initiated, and paid for institutional and policy change. Crawford ( 2017 ) examines the legacy of this key reframing. She develops a six-stage model of potential international responses to incidents of wartime violence. This model is fairly easily converted to working hypotheses and sub-hypotheses. Table  3 shows her model as a set of (non-relational) working hypotheses. She applied this model as a way to gather evidence among cases (e.g., the US response to sexual violence in the Democratic Republic of the Congo) to show the official level of response to sexual violence. Each case study chapter examined evidence to establish whether the case fit the pattern formalized in the working hypotheses. The framework was very useful in her comparative context. The framework allowed for consistent comparative analysis across cases. Her analysis of the three cases went well beyond the material covered in the framework. She freely incorporated useful inductively informed data in her analysis and discussion. The framework, however, allowed for alignment within and across cases.

9 Conclusion

In this article we argued that the exploratory research is also well suited for deductive approaches. By examining the landscape of deductive, exploratory research, we proposed the working hypothesis as a flexible conceptual framework and a useful tool for doing exploratory studies. It has the potential to guide and bring coherence across the steps in the research process. After presenting the nature of exploratory research purpose and how it differs from two types of research purposes identified in the literature—explanation, and description. We focused on answering four different questions in order to show the link between micro-conceptual frameworks and research purposes in a deductive setting. The answers to the four questions are summarized in Table  4 .

Firstly, we argued that working hypothesis and exploration are situated within the pragmatic philosophical perspective. Pragmatism allows for pluralism in theory and data collection techniques, which is compatible with the flexible exploratory purpose. Secondly, after introducing and discussing the four core elements of pragmatism (practical, pluralism, participatory, and provisional), we explained how the working hypothesis informs the methodologies and evidence collection of deductive exploratory research through a presentation of the benefits of triangulation provided by mixed methods research. Thirdly, as is clear from the article title, we introduced the working hypothesis as the micro-conceptual framework for deductive explorative research. We argued that the hypotheses of explorative research, which we call working hypotheses are distinguished from those of the explanatory research, since they do not require a relational component and are not bound by relational expectations. A working hypothesis is extremely flexible and idiosyncratic, and it could be viewed as a statement or group of statements of expectations tested in action depending on the research question. Using examples, we concluded by explaining how working hypotheses inform data collection and analysis for deductive exploratory research.

Crawford’s ( 2017 ) example showed how the structure of working hypotheses provide a framework for comparative case studies. Her criteria for analysis were specified ahead of time and used to frame each case. Thus, her comparisons were systemized across cases. Further, the framework ensured a connection between the data analysis and the literature review. Yet the flexible, working nature of the hypotheses allowed for unexpected findings to be discovered.

The evidence required to test working hypotheses is directed by the research purpose and potentially includes both quantitative and qualitative sources. Thus, all types of evidence, including quantitative methods should be part of the toolbox of deductive, explorative research. We show how the working hypotheses, as a flexible exploratory framework, resolves many seeming dualisms pervasive in the research methods literature.

To conclude, this article has provided an in-depth examination of working hypotheses taking into account philosophical questions and the larger formal research methods literature. By discussing working hypotheses as applied, theoretical tools, we demonstrated that working hypotheses fill a unique niche in the methods literature, since they provide a way to enhance alignment in deductive, explorative studies.

In practice, quantitative scholars often run multivariate analysis on data bases to find out if there are correlations. Hypotheses are tested because the statistical software does the math, not because the scholar has an a priori, relational expectation (hypothesis) well-grounded in the literature and supported by cogent arguments. Hunches are just fine. This is clearly an inductive approach to research and part of the large process of inquiry.

In 1958 , Philosophers of Science, Oppenheim and Putnam use the notion of Working Hypothesis in their title “Unity of Science as Working Hypothesis.” They too, use it as a big picture concept, “unity of science in this sense, can be fully realized constitutes an over-arching meta-scientific hypothesis, which enables one to see a unity in scientific activities that might otherwise appear disconnected or unrelated” (p. 4).

It should be noted that the positivism described in the research methods literature does not resemble philosophical positivism as developed by philosophers like Comte (Whetsell and Shields 2015 ). In the research methods literature “positivism means different things to different people….The term has long been emptied of any precise denotation …and is sometimes affixed to positions actually opposed to those espoused by the philosophers from whom the name derives” (Schrag 1992 , p. 5). For purposes of this paper, we are capturing a few essential ways positivism is presented in the research methods literature. This helps us to position the “working hypothesis” and “exploratory” research within the larger context in contemporary research methods. We are not arguing that the positivism presented here is anything more. The incompatibility theory discussed later, is an outgrowth of this research methods literature…

It should be noted that quantitative researchers often use inductive reasoning. They do this with existing data sets when they run correlations or regression analysis as a way to find relationships. They ask, what does the data tell us?

Qualitative researchers are also associated with phenomenology, hermeneutics, naturalistic inquiry and constructivism.

See Feilzer ( 2010 ), Howe ( 1988 ), Johnson and Onwuegbunzie ( 2004 ), Morgan ( 2007 ), Onwuegbuzie and Leech ( 2005 ), Biddle and Schafft ( 2015 ).

The term conceptual framework is applicable in a broad context (see Ravitch and Riggan 2012 ). The micro-conceptual framework narrows to the specific study and informs data collection (Shields and Rangarajan 2013 ; Shields et al. 2019a ) .

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Acknowledgements

The authors contributed equally to this work. The authors would like to thank Quality & Quantity’ s editors and the anonymous reviewers for their valuable advice and comments on previous versions of this paper.

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Casula, M., Rangarajan, N. & Shields, P. The potential of working hypotheses for deductive exploratory research. Qual Quant 55 , 1703–1725 (2021). https://doi.org/10.1007/s11135-020-01072-9

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Development of Working Hypothesis: An Integral Step in Research Methodology

Table of Contents

A working hypothesis is a preliminary assumption or proposition that is subject to further testing and verification. It is an integral component of the research process as it guides the researcher in defining the research problem and designing the research methodology. The working hypothesis is not a definitive statement, but rather a starting point for further investigation.

Importance of a Working Hypothesis

Developing a working hypothesis is essential for a successful research project. It helps the researcher in the following ways:

• Guides research design : A well-defined working hypothesis can guide the selection of the appropriate research design and methodology.
• Defines the research problem : The working hypothesis can help in defining the research problem and determining the scope of the research project.
• Provides a framework for analysis : The working hypothesis provides a framework for data analysis, helping the researcher in interpreting the results of the study.

Steps in Developing a Working Hypothesis

Here are the steps involved in developing a working hypothesis:

Step 1: Identify the Research Problem

The first step in developing a working hypothesis is to identify the research problem. The research problem should be clearly defined and should align with the research question or objective.

Step 2: Conduct a Literature Review

Conducting a literature review can help in identifying existing theories, models, and concepts related to the research problem. The literature review can provide insights into the factors that influence the research problem and can help in developing a preliminary assumption.

Step 3: Formulate a Preliminary Assumption

Based on the literature review, formulate a preliminary assumption or proposition that explains the relationship between the variables of interest.

Step 4: Refine the Assumption

Refine the preliminary assumption by revising and testing it against available evidence. The assumption should be specific, testable, and capable of generating meaningful results.

Step 5: Develop the Working Hypothesis

Finally, develop the working hypothesis based on the refined assumption. The working hypothesis should be clear, concise, and provide direction for the research project.

Developing a working hypothesis is a crucial step in the research process. It provides direction and focus for the research project and helps in defining the research problem and methodology. By following the steps outlined above, you can develop a meaningful and testable working hypothesis for your research project.

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Research Methodology for Management Decisions

1 Research Methodology: An Overview

• Meaning of Research
• Research Methodology
• Research Method
• Business Research Method
• Types of Research
• Importance of business research
• Role of research in important areas

2 Steps for Research Process

• Research process
• Define research problems
• Research Problem as Hypothesis Testing
• Extensive literature review in research
• Development of working hypothesis
• Preparing the research design
• Collecting the data
• Analysis of data
• Preparation of the report or the thesis

3 Research Designs

• Functions and Goals of Research Design
• Characteristics of a Good Design
• Different Types of Research Designs
• Exploratory Research Design
• Descriptive Research Design
• Experimental Research Design
• Types of Experimental Designs

4 Methods and Techniques of Data Collection

• Primary and Secondary Data
• Methods of Collecting Primary Data
• Merits and Demerits of Different Methods of Collecting Primary Data
• Designing a Questionnaire
• Pretesting a Questionnaire
• Editing of Primary Data
• Technique of Interview
• Collection of Secondary Data
• Scrutiny of Secondary Data

5 Attitude Measurement and Scales

• Attitudes, Attributes and Beliefs
• Issues in Attitude Measurement
• Scaling of Attitudes
• Deterministic Attitude Measurement Models: The Guttman Scale
• Thurstone’s Equal-Appearing Interval Scale
• The Semantic Differential Scale
• Summative Models: The Likert Scale
• The Q-Sort Technique
• Multidimensional Scaling
• Selection of an Appropriate Attitude Measurement Scale
• Limitations of Attitude Measurement Scales

6 Questionnaire Designing

• Introductory decisions
• Contents of the questionnaire
• Format of the questionnaire
• Steps involved in the questionnaire
• Structure and Design of Questionnaire
• Management of Fieldwork
• Ambiguities in the Questionnaire Methods

7 Sampling and Sampling Design

• Advantage of Sampling Over Census
• Simple Random Sampling
• Sampling Frame
• Probabilistic As pects of Sampling
• Stratified Random Sampling
• Other Methods of Sampling
• Sampling Design
• Non-Probability Sampling Methods

8 Data Processing

• Editing of Data
• Coding of Data
• Classification of Data
• Statistical Series
• Tables as Data Presentation Devices
• Graphical Presentation of Data

9 Statistical Analysis and Interpretation of Data: Nonparametric Tests

• One Sample Tests
• Two Sample Tests
• K Sample Tests

10 Multivariate Analysis of Data

• Regression Analysis
• Discriminant Analysis
• Factor Analysis

11 Ethics in Research

• Principles of research ethics
• Advantages of research ethics
• Limitations of the research ethics
• Steps involved in ethics
• What are research misconducts?

12 Substance of Reports

• Research Proposal
• Categories of Report
• Reviewing the Draft

13 Formats of Reports

• Parts of a Report
• Cover and Title Page
• Introductory Pages
• Reference Section
• Typing Instructions
• Copy Reading
• Proof Reading

14 Presentation of a Report

• Communication Dimensions
• Presentation Package
• Audio-Visual Aids
• Presenter’s Poise

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

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Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 13 Jun. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

Table of Content

What is Hypothesis?

Hypothesis meaning, characteristics of hypothesis, sources of hypothesis, types of hypothesis, simple hypothesis, complex hypothesis, directional hypothesis, non-directional hypothesis, null hypothesis (h0), alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis, hypothesis examples, simple hypothesis example, complex hypothesis example, directional hypothesis example, non-directional hypothesis example, alternative hypothesis (ha), functions of hypothesis, how hypothesis help in scientific research.

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Here are some common types of hypotheses:

• Non-directional Hypothesis

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Following are the examples of hypotheses based on their types:

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.
• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.
• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.
• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.
• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.

The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .

The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.

Hypothesis – FAQs

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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Meaning of hypothesis in English

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• abstraction
• accepted wisdom
• afterthought
• anthropocentrism
• determinist
• non-dogmatic
• non-empirical
• social Darwinism
• supersensible
• the domino theory

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