are small zooplankton found in freshwater inland lakes and are thought to switch their mode of reproduction from asexual to sexual in response to extreme temperatures (Mitchell 1999). Lakes containing have an average summer surface temperature of 20°C (Harper 1995) but may increase by more than 15% when expose to warm water effluent from power plants, paper mills, and chemical industry (Baker et al. 2000). Could an increase in lake temperature caused by industrial thermal pollution affect the survivorship and reproduction of ?
The sex of is mediated by the environment rather than genetics. Under optimal environmental conditions, populations consist of asexually reproducing females. When the environment shifts may be queued to reproduce sexually resulting in the production of male offspring and females carrying haploid eggs in sacs called ephippia (Mitchell 1999).
The purpose of this laboratory study is to examine the effects of increased water temperature on survivorship and reproduction. This study will help us characterize the magnitude of environmental change required to induce the onset of the sexual life cycle in . Because are known to be a sensitive environmental indicator species (Baker et al. 2000) and share similar structural and physiological features with many aquatic species, they serve as a good model for examining the effects of increasing water temperature on reproduction in a variety of aquatic invertebrates.
We hypothesized that populations reared in water temperatures ranging from 24-26 °C would have lower survivorship, higher male/female ratio among the offspring, and more female offspring carrying ephippia as compared with grown in water temperatures of 20-22°C. To test this hypothesis we reared populations in tanks containing water at either 24 +/- 2°C or 20 +/- 2°C. Over 10 days, we monitored survivorship, determined the sex of the offspring, and counted the number of female offspring containing ephippia.
Comments:
Background information
· Opening paragraph provides good focus immediately. The study organism, gender switching response, and temperature influence are mentioned in the first sentence. Although it does a good job documenting average lake water temperature and changes due to industrial run-off, it fails to make an argument that the 15% increase in lake temperature could be considered “extreme” temperature change.
· The study question is nicely embedded within relevant, well-cited background information. Alternatively, it could be stated as the first sentence in the introduction, or after all background information has been discussed before the hypothesis.
Rationale
· Good. Well-defined purpose for study; to examine the degree of environmental change necessary to induce the Daphnia sexual life
cycle.
How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.
0 = inadequate (C, D or F) | 1 = adequate (BC) | 2 = good (B) | 3 = very good (AB) | 4 = excellent (A) | |
Introduction BIG PICTURE: Did the Intro convey why experiment was performed and what it was designed to test?
| Introduction provides little to no relevant information. (This often results in a hypothesis that “comes out of nowhere.”) | Many key components are very weak or missing; those stated are unclear and/or are not stated concisely. Weak/missing components make it difficult to follow the rest of the paper. e.g., background information is not focused on a specific question and minimal biological rationale is presented such that hypothesis isn’t entirely logical
| Covers most key components but could be done much more logically, clearly, and/or concisely. e.g., biological rationale not fully developed but still supports hypothesis. Remaining components are done reasonably well, though there is still room for improvement. | Concisely & clearly covers all but one key component (w/ exception of rationale; see left) clearly covers all key components but could be a little more concise and/or clear. e.g., has done a reasonably nice job with the Intro but fails to state the approach OR has done a nice job with Intro but has also included some irrelevant background information
| Clearly, concisely, & logically presents all key components: relevant & correctly cited background information, question, biological rationale, hypothesis, approach. |
Our editors will review what you’ve submitted and determine whether to revise the article.
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 ).
It's the initial building block in the scientific method.
What makes a hypothesis testable.
Bibliography.
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research.
The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).
A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.
A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .
Here are some examples of hypothesis statements:
A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."
An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.
In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .
For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."
If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (BCcampus, 2015).
There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.
Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley .
A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.
The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.
"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts."
Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis
Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.
California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm
Karl Popper, "Conjectures and Refutations," Routledge, 1963.
Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.
University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf
William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/
University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf
University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19
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Writing a scientific paper is very similar to writing a lab report. The structure of each is primarily the same, but the purpose of each is different. Lab reports are meant to reflect understanding of the material and learn something new, while scientific papers are meant to contribute knowledge to a field of study. A scientific paper is broken down into eight sections: title, abstract, introduction, methods, results, discussion, conclusion, and references.
Introduction
Methods and Materials
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Visually hypothesising in scientific paper writing: confirming and refuting qualitative research hypotheses using diagrams.
2. overview of visual communication and post-positivist research, visual communication in post-positivist qualitative research, 3. understanding qualitative research (and hypotheses): types, notions, contestations and epistemological underpinnings, 3.1. what is qualitative research, 3.2. types of qualitative research and their epistemological underpinnings, 3.3. what is a research hypothesis can it be used in qualitative research, 3.4. analogical arguments in support of using hypotheses in qualitative research, 3.5. can a hypothesis be “tested” in qualitative research, 4. the process of developing and using hypotheses in qualitative research.
“Begin a research study without having to test a hypothesis. Instead, it allows them to develop hypotheses by listening to what the research participants say. Because the method involves developing hypotheses after the data are collected, it is called hypothesis-generating research rather than hypothesis-testing research. The grounded theory method uses two basic principles: (1) questioning rather than measuring, and (2) generating hypotheses using theoretical coding.”
“A core development concern in Nigeria is the magnitude of challenges rural people face. Inefficient infrastructures, lack of employment opportunities and poor social amenities are some of these challenges. These challenges persist mainly due to ineffective approaches used in tackling them. This research argues that an approach based on territorial development would produce better outcomes. The reason is that territorial development adopts integrated policies and actions with a focus on places as opposed to sectoral approaches. The research objectives were to evaluate rural development approaches and identify a specific approach capable of activating poverty reduction. It addressed questions bordering on past rural development approaches and how to improve urban-rural linkages in rural areas. It also addressed questions relating to ways that rural areas can reduce poverty through territorial development…” [ 16 ], p. 1
“Nigeria has legal and institutional opportunities for comprehensive improvement of rural areas through territorial development. However, due to the absence of a concrete rural development plan and area-based rural development strategies, this has not been materialized”.
Acknowledgments, conflicts of interest.
Types | Approach to Research or Enquiries | Data Collection Methods | Data Analysis Methods | Forms in Scientific Writing | Epistemological Foundations |
---|---|---|---|---|---|
Narrative | Explores situations, scenarios and processes | Interviews and documents | Storytelling, content review and theme (meaning development | In-depth narration of events or situations | Objectivism, postmodernism, social constructionism, feminism and constructivism (including interpretive and reflexive) in positivist and post-positivist perspectives |
Case study | Examination of episodic events with focus on answering “how” questions | Interviews, observations, document contents and physical inspections | Detailed identification of themes and development of narratives | In-depth study of possible lessons learned from a case or cases | |
Grounded theory | Investigates procedures | Interviews and questionnaire | Data coding, categorisation of themes and description of implications | Theory and theoretical models | |
Historical | Description of past events | Interviews, surveys and documents | Description of events development | Historical reports | |
Phenomenological | Understand or explain experiences | Interviews, surveys and observations | Description of experiences, examination of meanings and theme development | Contextualisation and reporting of experience | |
Ethnographic | Describes and interprets social grouping or cultural situation | Interviews, observations and active participation | Description and interpretation of data and theme development | Detailed reporting of interpreted data |
Chigbu, U.E. Visually Hypothesising in Scientific Paper Writing: Confirming and Refuting Qualitative Research Hypotheses Using Diagrams. Publications 2019 , 7 , 22. https://doi.org/10.3390/publications7010022
Chigbu UE. Visually Hypothesising in Scientific Paper Writing: Confirming and Refuting Qualitative Research Hypotheses Using Diagrams. Publications . 2019; 7(1):22. https://doi.org/10.3390/publications7010022
Chigbu, Uchendu Eugene. 2019. "Visually Hypothesising in Scientific Paper Writing: Confirming and Refuting Qualitative Research Hypotheses Using Diagrams" Publications 7, no. 1: 22. https://doi.org/10.3390/publications7010022
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Course: biology library > unit 1, the scientific method.
1. make an observation., 2. ask a question., 3. propose a hypothesis., 4. make predictions., 5. test the predictions..
Practical possibility, building a body of evidence, 6. iterate..
Introduction
Typically scientific journal articles have the following sections:
Materials & Methods
References used:
Kotsis, S.V. and Chung, K.C. (2010) A Guide for Writing in the Scientific Forum. Plastic and Reconstructive Surgery. 126(5):1763-71. PubMed ID: 21042135
Van Way, C.W. (2007) Writing a Scientific Paper. Nutrition in Clinical Practice. 22: 663-40. PubMed ID: 1804295
What to include:
Additional resources: Fisher, W. E. (2005) Abstract Writing. Journal of Surgical Research. 128(2):162-4. PubMed ID: 16165161 Peh, W.C. and Ng, K.H. (2008) Abstract and keywords. Singapore Medical Journal. 49(9): 664-6. PubMed ID: 18830537
Additional resources: Annesley, T. M. (2010) "It was a cold and rainy night": set the scene with a good introduction. Clinical Chemistry. 56(5):708-13. PubMed ID: 20207764 Peh, W.C. and Ng, K.H. (2008) Writing the introduction. Singapore Medical Journal. 49(10):756-8. PubMed ID: 18946606
Additional resources: Lallet, R. H. (2004) How to write the methods section of a research paper. Respiratory Care. 49(10): 1229-32. PubMed ID: 15447808 Ng, K.H. and Peh, W.C. (2008) Writing the materials and methods. Singapore Medical Journal. 49(11): 856-9. PubMed ID: 19037549
Additional resources: Ng, K.H and Peh, W.C. (2008) Writing the results. Singapore Medical Journal. 49(12):967-9. PubMed ID: 19122944 Streiner, D.L. (2007) A shortcut to rejection: how not to write the results section of a paper. Canadian Journal of Psychiatry. 52(6):385-9. PubMed ID: 17696025
Additional resources: Annesley, T. M. (2010) The discussion section: your closing argument. Clinical Chemistry. 56(11):1671-4. PubMed ID: 20833779 Ng, K.H. and Peh, W.C. (2009) Writing the discussion. Singapore Medical Journal. 50(5):458-61. PubMed ID: 19495512
Tables & Figures: Durbin, C. G. (2004) Effective use of tables and figures in abstracts, presentations, and papers. Respiratory Care. 49(10): 1233-7. PubMed ID: 15447809 Ng, K. H. and Peh, W.C.G. (2009) Preparing effective tables. Singapore Medical Journal. (50)2: 117-9. PubMed ID: 19296024
Statistics: Ng, K. H. and Peh, W.C.G. (2009) Presenting the statistical results. Singapore Medical Journal. (50)1: 11-4. PubMed ID: 19224078
References: Peh, W.C.G. and Ng, K. H. (2009) Preparing the references. Singapore Medical Journal. (50)7: 11-4. PubMed ID: 19644619
Saul Mcleod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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The p-value in statistics quantifies the evidence against a null hypothesis. A low p-value suggests data is inconsistent with the null, potentially favoring an alternative hypothesis. Common significance thresholds are 0.05 or 0.01.
When you perform a statistical test, a p-value helps you determine the significance of your results in relation to the null hypothesis.
The null hypothesis (H0) states no relationship exists between the two variables being studied (one variable does not affect the other). It states the results are due to chance and are not significant in supporting the idea being investigated. Thus, the null hypothesis assumes that whatever you try to prove did not happen.
The alternative hypothesis (Ha or H1) is the one you would believe if the null hypothesis is concluded to be untrue.
The alternative hypothesis states that the independent variable affected the dependent variable, and the results are significant in supporting the theory being investigated (i.e., the results are not due to random chance).
A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e., that the null hypothesis is true).
The level of statistical significance is often expressed as a p-value between 0 and 1.
The smaller the p -value, the less likely the results occurred by random chance, and the stronger the evidence that you should reject the null hypothesis.
Remember, a p-value doesn’t tell you if the null hypothesis is true or false. It just tells you how likely you’d see the data you observed (or more extreme data) if the null hypothesis was true. It’s a piece of evidence, not a definitive proof.
Suppose you’re conducting a study to determine whether a new drug has an effect on pain relief compared to a placebo. If the new drug has no impact, your test statistic will be close to the one predicted by the null hypothesis (no difference between the drug and placebo groups), and the resulting p-value will be close to 1. It may not be precisely 1 because real-world variations may exist. Conversely, if the new drug indeed reduces pain significantly, your test statistic will diverge further from what’s expected under the null hypothesis, and the p-value will decrease. The p-value will never reach zero because there’s always a slim possibility, though highly improbable, that the observed results occurred by random chance.
The significance level (alpha) is a set probability threshold (often 0.05), while the p-value is the probability you calculate based on your study or analysis.
A p-value less than or equal to a predetermined significance level (often 0.05 or 0.01) indicates a statistically significant result, meaning the observed data provide strong evidence against the null hypothesis.
This suggests the effect under study likely represents a real relationship rather than just random chance.
For instance, if you set α = 0.05, you would reject the null hypothesis if your p -value ≤ 0.05.
It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).
Therefore, we reject the null hypothesis and accept the alternative hypothesis.
Upon analyzing the pain relief effects of the new drug compared to the placebo, the computed p-value is less than 0.01, which falls well below the predetermined alpha value of 0.05. Consequently, you conclude that there is a statistically significant difference in pain relief between the new drug and the placebo.
A p-value of 0.001 is highly statistically significant beyond the commonly used 0.05 threshold. It indicates strong evidence of a real effect or difference, rather than just random variation.
Specifically, a p-value of 0.001 means there is only a 0.1% chance of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is correct.
Such a small p-value provides strong evidence against the null hypothesis, leading to rejecting the null in favor of the alternative hypothesis.
This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis; we can only reject it or fail to reject it.
Note : when the p-value is above your threshold of significance, it does not mean that there is a 95% probability that the alternative hypothesis is true.
Most statistical software packages like R, SPSS, and others automatically calculate your p-value. This is the easiest and most common way.
Online resources and tables are available to estimate the p-value based on your test statistic and degrees of freedom.
These tables help you understand how often you would expect to see your test statistic under the null hypothesis.
Understanding the Statistical Test:
Different statistical tests are designed to answer specific research questions or hypotheses. Each test has its own underlying assumptions and characteristics.
For example, you might use a t-test to compare means, a chi-squared test for categorical data, or a correlation test to measure the strength of a relationship between variables.
Be aware that the number of independent variables you include in your analysis can influence the magnitude of the test statistic needed to produce the same p-value.
This factor is particularly important to consider when comparing results across different analyses.
If you’re comparing the effectiveness of just two different drugs in pain relief, a two-sample t-test is a suitable choice for comparing these two groups. However, when you’re examining the impact of three or more drugs, it’s more appropriate to employ an Analysis of Variance ( ANOVA) . Utilizing multiple pairwise comparisons in such cases can lead to artificially low p-values and an overestimation of the significance of differences between the drug groups.
A statistically significant result cannot prove that a research hypothesis is correct (which implies 100% certainty).
Instead, we may state our results “provide support for” or “give evidence for” our research hypothesis (as there is still a slight probability that the results occurred by chance and the null hypothesis was correct – e.g., less than 5%).
In our comparison of the pain relief effects of the new drug and the placebo, we observed that participants in the drug group experienced a significant reduction in pain ( M = 3.5; SD = 0.8) compared to those in the placebo group ( M = 5.2; SD = 0.7), resulting in an average difference of 1.7 points on the pain scale (t(98) = -9.36; p < 0.001).
The 6th edition of the APA style manual (American Psychological Association, 2010) states the following on the topic of reporting p-values:
“When reporting p values, report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001.
The tradition of reporting p values in the form p < .10, p < .05, p < .01, and so forth, was appropriate in a time when only limited tables of critical values were available.” (p. 114)
A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables.
However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).
To understand the strength of the difference between the two groups (control vs. experimental) a researcher needs to calculate the effect size .
In statistical hypothesis testing, you reject the null hypothesis when the p-value is less than or equal to the significance level (α) you set before conducting your test. The significance level is the probability of rejecting the null hypothesis when it is true. Commonly used significance levels are 0.01, 0.05, and 0.10.
Remember, rejecting the null hypothesis doesn’t prove the alternative hypothesis; it just suggests that the alternative hypothesis may be plausible given the observed data.
The p -value is conditional upon the null hypothesis being true but is unrelated to the truth or falsity of the alternative hypothesis.
If your p-value is less than or equal to 0.05 (the significance level), you would conclude that your result is statistically significant. This means the evidence is strong enough to reject the null hypothesis in favor of the alternative hypothesis.
No, not all p-values below 0.05 are considered statistically significant. The threshold of 0.05 is commonly used, but it’s just a convention. Statistical significance depends on factors like the study design, sample size, and the magnitude of the observed effect.
A p-value below 0.05 means there is evidence against the null hypothesis, suggesting a real effect. However, it’s essential to consider the context and other factors when interpreting results.
Researchers also look at effect size and confidence intervals to determine the practical significance and reliability of findings.
Sample size can impact the interpretation of p-values. A larger sample size provides more reliable and precise estimates of the population, leading to narrower confidence intervals.
With a larger sample, even small differences between groups or effects can become statistically significant, yielding lower p-values. In contrast, smaller sample sizes may not have enough statistical power to detect smaller effects, resulting in higher p-values.
Therefore, a larger sample size increases the chances of finding statistically significant results when there is a genuine effect, making the findings more trustworthy and robust.
No, a non-significant p-value does not necessarily indicate that there is no effect or difference in the data. It means that the observed data do not provide strong enough evidence to reject the null hypothesis.
There could still be a real effect or difference, but it might be smaller or more variable than the study was able to detect.
Other factors like sample size, study design, and measurement precision can influence the p-value. It’s important to consider the entire body of evidence and not rely solely on p-values when interpreting research findings.
While a p-value can be extremely small, it cannot technically be absolute zero. When a p-value is reported as p = 0.000, the actual p-value is too small for the software to display. This is often interpreted as strong evidence against the null hypothesis. For p values less than 0.001, report as p < .001
How science REALLY works...
Misconception: Science proves ideas.
Misconception: Science can only disprove ideas.
Correction: Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives. Read more about it.
In this case, the term argument refers not to a disagreement between two people, but to an evidence-based line of reasoning — so scientific arguments are more like the closing argument in a court case (a logical description of what we think and why we think it) than they are like the fights you may have had with siblings. Scientific arguments involve three components: the idea (a hypothesis or theory), the expectations generated by that idea (frequently called predictions), and the actual observations relevant to those expectations (the evidence). These components are always related in the same logical way:
When scientists describe their arguments, they frequently talk about their expectations in terms of what a hypothesis or theory predicts: “If it were the case that smoking causes lung cancer, then we’d predict that countries with higher rates of smoking would have higher rates of lung cancer.” At first, it might seem confusing to talk about a prediction that doesn’t deal with the future, but that refers to something going on right now or that may have already happened. In fact, this is just another way of discussing the expectations that the hypothesis or theory generates. So when a scientist talks about the predicted rates of lung cancer, he or she really means something like “the rates that we’d expect to see if our hypothesis were correct.”
If the idea generates expectations that hold true (are actually observed), then the idea is more likely to be accurate. If the idea generates expectations that don’t hold true (are not observed), then we are less likely to accept the idea. For example, consider the idea that cells are the building blocks of life. If that idea were true, we’d expect to see cells in all kinds of living tissues observed under a microscope — that’s our expected observation. In fact, we do observe this (our actual observation), so evidence supports the idea that living things are built from cells.
Though the structure of this argument is consistent (hypothesis, then expectation, then actual observation), its pieces may be assembled in different orders. For example, the first observations of cells were made in the 1600s, but cell theory was not postulated until 200 years later — so in this case, the evidence actually helped inspire the idea. Whether the idea comes first or the evidence comes first, the logic relating them remains the same.
Here, we’ll explore scientific arguments and how to build them. You can investigate:
Putting the pieces together: The hard work of building arguments
Or just click the Next button to dive right in!
Scientific arguments rely on testable ideas. To learn what makes an idea testable, review our Science Checklist .
Summing up the process
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Biophysical evidence to support and extend the vitamin d‐folate hypothesis as a paradigm for the evolution of human skin pigmentation, the evolution of human skin pigmentation involved the interactions of genetic, environmental, and cultural variables, skin colour and vitamin d: an update, vitamin d and evolution: pharmacologic implications., women with fair phenotypes seem to confer a survival advantage in a low uv milieu. a nested matched case control study, evolution, prehistory and vitamin d, weaker bones and white skin as adaptions to improve anthropological “fitness” for northern environments, the vitamin d-folate hypothesis in human vascular health., evolutionary perspective in rickets and vitamin d, nutrition and its role in human evolution, related papers.
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A consensus has arisen in the astronomical community that familiar matter made of atoms is not the dominant form of matter in the Universe. Instead, an invisible form of matter, called dark matter , is thought to be far more prevalent. However, a small group of researchers deny the existence of dark matter, instead saying our understanding of how objects move is incomplete. A recent paper in the Monthly Notices of the Royal Astronomical Society seems to have ruled this out definitively.
Stars, planets, and galaxies move under the direction of the force of gravity, and Isaac Newton worked out the laws that govern that motion, which we now call Newtonian dynamics. However, despite the enormous success of Newtonian dynamics, this success is not universal. Indeed, when Newton’s equations are applied to certain astronomical phenomena, they do not make the correct predictions. One such example is the speed at which galaxies rotate. When astronomers measure the speed of stars in the periphery of a galaxy, they move faster than can be explained by accepted theory. Instead, the galaxies should fly apart.
The solution to this mystery favored by most scientists is that beyond the familiar stars and clouds of gas, our galaxy also hosts a large amount of invisible matter, called dark matter. This dark matter adds to the gravitational force holding the galaxy together. Thus, the evidence for dark matter is indirect. It has never been observed in the laboratory; yet its ability to explain the motion of galaxies is strong circumstantial evidence that it exists.
Still, because dark matter remains unobserved, alternative hypotheses should be considered. One idea, called MOND (Modified Newtonian Dynamics), suggests that the Newtonian laws of motion taught in introductory physics classes are not quite right. For accelerations larger than about 10 -11 times the gravity felt on the surface of Earth, Newton’s familiar equations work. For accelerations smaller than that, a new set of equations applies. MOND theory was first devised by Israeli physicist Mordehai Milgrom in 1983, and while the model is not accepted by the majority of astronomers, it has some passionate supporters .
When astronomers apply MOND theory to predicting the rotation of galaxies, it works quite well, essentially as well as dark matter theory does. Thus, a measurement is needed that will definitively distinguish between the two.
In the newly released paper, researchers used data recorded using the Gaia satellite to study wide binary stars , which are two stars that orbit one another at large distances. In this study, binary stars were included if their separation was in the range of 2,000 to 30,000 times the average separation between Earth and the Sun. Binary stars with these characteristics experience a range of accelerations that allow scientists to try to determine if MOND or Newtonian theory is correct.
So, what did they find? The study very clearly favors Newtonian theory over MOND as an accurate description of the orbital behavior of wide binary stars . (The measurement ruled out MOND by sixteen sigma, which is far larger than a five-sigma result that is considered definitive.)
In their paper, researchers also tackled earlier reports that wide binaries actually supported the MOND hypothesis. They separated their data into wide binaries in which the measurements were precise and ones in which there was significant uncertainty in the numbers. They found that an analysis that included poorly measured wide binaries favored MOND, but when only precisely measured results were included, the data strongly favored Newtonian dynamics.
Does this measurement prove that dark matter is real? No. That would be too strong of a conclusion. If confirmed, what it demonstrates is that the specific theory called MOND is incorrect. It does not rule out all alternative theories to dark matter. Others remain viable. Indeed, there are other proposed solutions to the mystery of rapidly rotating galaxies, including changes to the laws of gravity, as well as different modifications to the laws of motion. In addition, while the distance separating wide binary stars is very large, it is very small compared to the size of galaxies. It remains possible that MOND theory could apply on galactic sizes, but not on the scale of large stellar systems.
Still, the result, if confirmed, is a very important advance in our search for the answer of why galaxies rotate so quickly. While not as satisfying as a definitive discovery, definitive refutations of other theories is how science advances. As fictional detective Sherlock Holmes once said in the story “The Sign of Four”: “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.”
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Title: what do language models learn in context the structured task hypothesis.
Abstract: Large language models (LLMs) exhibit an intriguing ability to learn a novel task from in-context examples presented in a demonstration, termed in-context learning (ICL). Understandably, a swath of research has been dedicated to uncovering the theories underpinning ICL. One popular hypothesis explains ICL by task selection. LLMs identify the task based on the demonstration and generalize it to the prompt. Another popular hypothesis is that ICL is a form of meta-learning, i.e., the models learn a learning algorithm at pre-training time and apply it to the demonstration. Finally, a third hypothesis argues that LLMs use the demonstration to select a composition of tasks learned during pre-training to perform ICL. In this paper, we empirically explore these three hypotheses that explain LLMs' ability to learn in context with a suite of experiments derived from common text classification tasks. We invalidate the first two hypotheses with counterexamples and provide evidence in support of the last hypothesis. Our results suggest an LLM could learn a novel task in context via composing tasks learned during pre-training.
Comments: | This work is published in ACL 2024 |
Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
Cite as: | [cs.CL] |
(or [cs.CL] for this version) | |
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A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989,10 is still attracting numerous citations on Scopus, the largest bibliographic database ...
Developing a hypothesis (with example) 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. Example: Research question.
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.
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 ...
Step 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. If a first-year student starts attending more lectures, then their exam scores will improve.
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.
Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper.
Another example for a directional one-tailed alternative hypothesis would be that. H1: Attending private classes before important exams has a positive effect on performance. Your null hypothesis would then be that. H0: Attending private classes before important exams has no/a negative effect on performance.
The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem. 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a 'if-then' structure. 3.
There are several potential sources for developing a good research paper hypothesis. Let's consider their details and examples: Scientific theories Hypotheses can stem from existing scientific theories. Suppose we have an established theory in psychology that suggests a positive correlation between sleep quality and cognitive performance.
Dr. Michelle Harris, Dr. Janet Batzli,Biocore. This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question, biological rationale, hypothesis, and general approach. If the Introduction is done well, there should be no question in the reader's mind why and on ...
hypothesis. science. 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 ...
Bibliography. A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an ...
Formulating Strong Hypotheses. Before you write your research hypothesis, make sure to do some reading in your area of interest; good resources will include scholarly papers, articles, books, and other academic research. Because your research hypothesis will be a specific, testable prediction about what you expect to happen in a study, you will ...
Scienti c papers are usually structured in four sections, that. is, introduction, material and methods, resul ts and discussion. Other common parts of manuscripts a re abstracts, the. reference ...
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
Introduction. The introduction of a scientific paper discusses the problem being studied and other theory that is relevant to understanding the findings. The hypothesis of the experiment and the motivation for the research are stated in this section. Write the introduction in your own words. Try not to copy from a lab manual or other guidelines.
This study argues fervently that the use of a hypothesis in science is not in any way limited to quantitative research. This can be presented in the following analogy. ... Uchendu Eugene. 2019. "Visually Hypothesising in Scientific Paper Writing: Confirming and Refuting Qualitative Research Hypotheses Using Diagrams" Publications 7, no. 1: 22 ...
The scientific method. At the core of biology and other sciences lies a problem-solving approach called the scientific method. The scientific method has five basic steps, plus one feedback step: Make an observation. Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis.
Overview. Typically scientific journal articles have the following sections: References used: Kotsis, S.V. and Chung, K.C. (2010) A Guide for Writing in the Scientific Forum. Plastic and Reconstructive Surgery. 126 (5):1763-71. PubMed ID: 21042135. Van Way, C.W. (2007) Writing a Scientific Paper. Nutrition in Clinical Practice. 22: 663-40.
A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e., that the null hypothesis is true). The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p -value, the less likely the results occurred by random chance, and the ...
This lesson is designed to guide your students through the steps of the scientific method (Figure 1) using a fun, hands-on project: paper rockets. You can read about the scientific method in much more detail in this guide. Image Credit: created by Amy Cowen for Science Buddies / Science Buddies. Figure 1. Steps of the scientific method.
Understanding Science 101. Testing ideas with evidence from the natural world is at the core of science. Scientific testing involves figuring out what we would expect to observe if an idea were correct and comparing that expectation to what we actually observe. Scientific arguments are built from an idea and the evidence relevant to that idea ...
A version of this story appeared in Science, Vol 384, Issue 6700. Authors of a landmark Alzheimer's disease research paper published in Nature in 2006 have agreed to retract the study in response to allegations of image manipulation. University of Minnesota (UMN) Twin Cities neuroscientist Karen Ashe, the paper's senior author, acknowledged ...
The literature search supported the hypothesis that through natural selection and intricate genetic adaptations, humans who migrated to areas with lower levels of UVR underwent a skin-lightening process to avoid the consequences of vitamin D deficiency. Understanding the genetic adaptations that occurred as humans migrated out of Africa to higher latitudes helps explain on a population-wide ...
A glymphatic system might still cleanse the brain, the researchers say, but sleep actually slows this cleansing down. Other researchers are stumped as to how to explain the opposing results, and several declined to comment on the record for fear of entering a heated debate. A few see the new findings as a serious blow to the sleep clearance ...
A new paper largely has eliminated MOND as a viable theory. A consensus has arisen in the astronomical community that familiar matter made of atoms is not the dominant form of matter in the ...
Large language models (LLMs) exhibit an intriguing ability to learn a novel task from in-context examples presented in a demonstration, termed in-context learning (ICL). Understandably, a swath of research has been dedicated to uncovering the theories underpinning ICL. One popular hypothesis explains ICL by task selection. LLMs identify the task based on the demonstration and generalize it to ...