Weekend batch
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
Free eBook: Top Programming Languages For A Data Scientist
Normality Test in Minitab: Minitab with Statistics
Machine Learning Career Guide: A Playbook to Becoming a Machine Learning Engineer
Hypothesis testing is a tool for making statistical inferences about the population data. It is an analysis tool that tests assumptions and determines how likely something is within a given standard of accuracy. Hypothesis testing provides a way to verify whether the results of an experiment are valid.
A null hypothesis and an alternative hypothesis are set up before performing the hypothesis testing. This helps to arrive at a conclusion regarding the sample obtained from the population. In this article, we will learn more about hypothesis testing, its types, steps to perform the testing, and associated examples.
1. | |
2. | |
3. | |
4. | |
5. | |
6. | |
7. | |
8. |
Hypothesis testing uses sample data from the population to draw useful conclusions regarding the population probability distribution . It tests an assumption made about the data using different types of hypothesis testing methodologies. The hypothesis testing results in either rejecting or not rejecting the null hypothesis.
Hypothesis testing can be defined as a statistical tool that is used to identify if the results of an experiment are meaningful or not. It involves setting up a null hypothesis and an alternative hypothesis. These two hypotheses will always be mutually exclusive. This means that if the null hypothesis is true then the alternative hypothesis is false and vice versa. An example of hypothesis testing is setting up a test to check if a new medicine works on a disease in a more efficient manner.
The null hypothesis is a concise mathematical statement that is used to indicate that there is no difference between two possibilities. In other words, there is no difference between certain characteristics of data. This hypothesis assumes that the outcomes of an experiment are based on chance alone. It is denoted as \(H_{0}\). Hypothesis testing is used to conclude if the null hypothesis can be rejected or not. Suppose an experiment is conducted to check if girls are shorter than boys at the age of 5. The null hypothesis will say that they are the same height.
The alternative hypothesis is an alternative to the null hypothesis. It is used to show that the observations of an experiment are due to some real effect. It indicates that there is a statistical significance between two possible outcomes and can be denoted as \(H_{1}\) or \(H_{a}\). For the above-mentioned example, the alternative hypothesis would be that girls are shorter than boys at the age of 5.
In hypothesis testing, the p value is used to indicate whether the results obtained after conducting a test are statistically significant or not. It also indicates the probability of making an error in rejecting or not rejecting the null hypothesis.This value is always a number between 0 and 1. The p value is compared to an alpha level, \(\alpha\) or significance level. The alpha level can be defined as the acceptable risk of incorrectly rejecting the null hypothesis. The alpha level is usually chosen between 1% to 5%.
All sets of values that lead to rejecting the null hypothesis lie in the critical region. Furthermore, the value that separates the critical region from the non-critical region is known as the critical value.
Depending upon the type of data available and the size, different types of hypothesis testing are used to determine whether the null hypothesis can be rejected or not. The hypothesis testing formula for some important test statistics are given below:
We will learn more about these test statistics in the upcoming section.
Selecting the correct test for performing hypothesis testing can be confusing. These tests are used to determine a test statistic on the basis of which the null hypothesis can either be rejected or not rejected. Some of the important tests used for hypothesis testing are given below.
A z test is a way of hypothesis testing that is used for a large sample size (n ≥ 30). It is used to determine whether there is a difference between the population mean and the sample mean when the population standard deviation is known. It can also be used to compare the mean of two samples. It is used to compute the z test statistic. The formulas are given as follows:
The t test is another method of hypothesis testing that is used for a small sample size (n < 30). It is also used to compare the sample mean and population mean. However, the population standard deviation is not known. Instead, the sample standard deviation is known. The mean of two samples can also be compared using the t test.
The Chi square test is a hypothesis testing method that is used to check whether the variables in a population are independent or not. It is used when the test statistic is chi-squared distributed.
One tailed hypothesis testing is done when the rejection region is only in one direction. It can also be known as directional hypothesis testing because the effects can be tested in one direction only. This type of testing is further classified into the right tailed test and left tailed test.
Right Tailed Hypothesis Testing
The right tail test is also known as the upper tail test. This test is used to check whether the population parameter is greater than some value. The null and alternative hypotheses for this test are given as follows:
\(H_{0}\): The population parameter is ≤ some value
\(H_{1}\): The population parameter is > some value.
If the test statistic has a greater value than the critical value then the null hypothesis is rejected
Left Tailed Hypothesis Testing
The left tail test is also known as the lower tail test. It is used to check whether the population parameter is less than some value. The hypotheses for this hypothesis testing can be written as follows:
\(H_{0}\): The population parameter is ≥ some value
\(H_{1}\): The population parameter is < some value.
The null hypothesis is rejected if the test statistic has a value lesser than the critical value.
In this hypothesis testing method, the critical region lies on both sides of the sampling distribution. It is also known as a non - directional hypothesis testing method. The two-tailed test is used when it needs to be determined if the population parameter is assumed to be different than some value. The hypotheses can be set up as follows:
\(H_{0}\): the population parameter = some value
\(H_{1}\): the population parameter ≠ some value
The null hypothesis is rejected if the test statistic has a value that is not equal to the critical value.
Hypothesis testing can be easily performed in five simple steps. The most important step is to correctly set up the hypotheses and identify the right method for hypothesis testing. The basic steps to perform hypothesis testing are as follows:
The best way to solve a problem on hypothesis testing is by applying the 5 steps mentioned in the previous section. Suppose a researcher claims that the mean average weight of men is greater than 100kgs with a standard deviation of 15kgs. 30 men are chosen with an average weight of 112.5 Kgs. Using hypothesis testing, check if there is enough evidence to support the researcher's claim. The confidence interval is given as 95%.
Step 1: This is an example of a right-tailed test. Set up the null hypothesis as \(H_{0}\): \(\mu\) = 100.
Step 2: The alternative hypothesis is given by \(H_{1}\): \(\mu\) > 100.
Step 3: As this is a one-tailed test, \(\alpha\) = 100% - 95% = 5%. This can be used to determine the critical value.
1 - \(\alpha\) = 1 - 0.05 = 0.95
0.95 gives the required area under the curve. Now using a normal distribution table, the area 0.95 is at z = 1.645. A similar process can be followed for a t-test. The only additional requirement is to calculate the degrees of freedom given by n - 1.
Step 4: Calculate the z test statistic. This is because the sample size is 30. Furthermore, the sample and population means are known along with the standard deviation.
z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\).
\(\mu\) = 100, \(\overline{x}\) = 112.5, n = 30, \(\sigma\) = 15
z = \(\frac{112.5-100}{\frac{15}{\sqrt{30}}}\) = 4.56
Step 5: Conclusion. As 4.56 > 1.645 thus, the null hypothesis can be rejected.
Confidence intervals form an important part of hypothesis testing. This is because the alpha level can be determined from a given confidence interval. Suppose a confidence interval is given as 95%. Subtract the confidence interval from 100%. This gives 100 - 95 = 5% or 0.05. This is the alpha value of a one-tailed hypothesis testing. To obtain the alpha value for a two-tailed hypothesis testing, divide this value by 2. This gives 0.05 / 2 = 0.025.
Related Articles:
Important Notes on Hypothesis Testing
go to slide go to slide go to slide
Book a Free Trial Class
What is hypothesis testing.
Hypothesis testing in statistics is a tool that is used to make inferences about the population data. It is also used to check if the results of an experiment are valid.
The z test in hypothesis testing is used to find the z test statistic for normally distributed data . The z test is used when the standard deviation of the population is known and the sample size is greater than or equal to 30.
The t test in hypothesis testing is used when the data follows a student t distribution . It is used when the sample size is less than 30 and standard deviation of the population is not known.
The formula for a one sample z test in hypothesis testing is z = \(\frac{\overline{x}-\mu}{\frac{\sigma}{\sqrt{n}}}\) and for two samples is z = \(\frac{(\overline{x_{1}}-\overline{x_{2}})-(\mu_{1}-\mu_{2})}{\sqrt{\frac{\sigma_{1}^{2}}{n_{1}}+\frac{\sigma_{2}^{2}}{n_{2}}}}\).
The p value helps to determine if the test results are statistically significant or not. In hypothesis testing, the null hypothesis can either be rejected or not rejected based on the comparison between the p value and the alpha level.
When the rejection region is only on one side of the distribution curve then it is known as one tail hypothesis testing. The right tail test and the left tail test are two types of directional hypothesis testing.
To get the alpha level in a two tail hypothesis testing divide \(\alpha\) by 2. This is done as there are two rejection regions in the curve.
An official website of the United States government
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .
Priya ranganathan.
1 Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India
2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India
The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors.
Ranganathan P, Pramesh CS. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231.
Two papers quoted in this issue of the Indian Journal of Critical Care Medicine report. The results of studies aim to prove that a new intervention is better than (superior to) an existing treatment. In the ABLE study, the investigators wanted to show that transfusion of fresh red blood cells would be superior to standard-issue red cells in reducing 90-day mortality in ICU patients. 1 The PROPPR study was designed to prove that transfusion of a lower ratio of plasma and platelets to red cells would be superior to a higher ratio in decreasing 24-hour and 30-day mortality in critically ill patients. 2 These studies are known as superiority studies (as opposed to noninferiority or equivalence studies which will be discussed in a subsequent article).
A sample represents a group of participants selected from the entire population. Since studies cannot be carried out on entire populations, researchers choose samples, which are representative of the population. This is similar to walking into a grocery store and examining a few grains of rice or wheat before purchasing an entire bag; we assume that the few grains that we select (the sample) are representative of the entire sack of grains (the population).
The results of the study are then extrapolated to generate inferences about the population. We do this using a process known as hypothesis testing. This means that the results of the study may not always be identical to the results we would expect to find in the population; i.e., there is the possibility that the study results may be erroneous.
A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate hypothesis. For superiority studies, the alternate hypothesis states that one treatment (usually the new or experimental treatment) is superior to the other; the null hypothesis states that there is no difference between the treatments (the treatments are equal). For example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs. We then state the alternate hypothesis—There is a difference between groups receiving fresh RBCs and standard-issue RBCs. It is important to note that we have stated that the groups are different, without specifying which group will be better than the other. This is known as a two-tailed hypothesis and it allows us to test for superiority on either side (using a two-sided test). This is because, when we start a study, we are not 100% certain that the new treatment can only be better than the standard treatment—it could be worse, and if it is so, the study should pick it up as well. One tailed hypothesis and one-sided statistical testing is done for non-inferiority studies, which will be discussed in a subsequent paper in this series.
There are two possibilities to consider when interpreting the results of a superiority study. The first possibility is that there is truly no difference between the treatments but the study finds that they are different. This is called a Type-1 error or false-positive error or alpha error. This means falsely rejecting the null hypothesis.
The second possibility is that there is a difference between the treatments and the study does not pick up this difference. This is called a Type 2 error or false-negative error or beta error. This means falsely accepting the null hypothesis.
The power of the study is the ability to detect a difference between groups and is the converse of the beta error; i.e., power = 1-beta error. Alpha and beta errors are finalized when the protocol is written and form the basis for sample size calculation for the study. In an ideal world, we would not like any error in the results of our study; however, we would need to do the study in the entire population (infinite sample size) to be able to get a 0% alpha and beta error. These two errors enable us to do studies with realistic sample sizes, with the compromise that there is a small possibility that the results may not always reflect the truth. The basis for this will be discussed in a subsequent paper in this series dealing with sample size calculation.
Conventionally, type 1 or alpha error is set at 5%. This means, that at the end of the study, if there is a difference between groups, we want to be 95% certain that this is a true difference and allow only a 5% probability that this difference has occurred by chance (false positive). Type 2 or beta error is usually set between 10% and 20%; therefore, the power of the study is 90% or 80%. This means that if there is a difference between groups, we want to be 80% (or 90%) certain that the study will detect that difference. For example, in the ABLE study, sample size was calculated with a type 1 error of 5% (two-sided) and power of 90% (type 2 error of 10%) (1).
Table 1 gives a summary of the two types of statistical errors with an example
Statistical errors
(a) Types of statistical errors | |||
: Null hypothesis is | |||
True | False | ||
Null hypothesis is actually | True | Correct results! | Falsely rejecting null hypothesis - Type I error |
False | Falsely accepting null hypothesis - Type II error | Correct results! | |
(b) Possible statistical errors in the ABLE trial | |||
There is difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | There difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | ||
Truth | There is difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | Correct results! | Falsely rejecting null hypothesis - Type I error |
There difference in mortality between groups receiving fresh RBCs and standard-issue RBCs | Falsely accepting null hypothesis - Type II error | Correct results! |
In the next article in this series, we will look at the meaning and interpretation of ‘ p ’ value and confidence intervals for hypothesis testing.
Source of support: Nil
Conflict of interest: None
LEARN STATISTICS EASILY
Learn Data Analysis Now!
You will learn the essentials of hypothesis tests, from fundamental concepts to practical applications in statistics.
Hypothesis testing is a statistical tool used to make decisions based on data.
It involves making assumptions about a population parameter and testing its validity using a population sample.
Hypothesis tests help us draw conclusions and make informed decisions in various fields like business, research, and science.
The null hypothesis (H0) is an initial claim about a population parameter, typically representing no effect or no difference.
The alternative hypothesis (H1) opposes the null hypothesis, suggesting an effect or difference.
Hypothesis tests aim to determine if there is evidence for the null hypothesis rejection in favor of the alternative hypothesis.
The significance level (α), often set at 0.05 or 5%, serves as a threshold for determining if we should reject the null hypothesis.
A p-value, calculated during hypothesis testing, represents the probability of observing the test statistic if the null hypothesis is true.
Suppose the p-value is less than the significance level. We reject the null hypothesis, in that case, indicating that the alternative hypothesis is more likely.
Parametric tests assume the data follows a specific probability distribution, usually the normal distribution. Examples include the Student’s t-test.
Non-parametric tests do not require such assumptions and are helpful when dealing with data that do not meet the assumptions of parametric tests. Examples include the Mann-Whitney U test.
🎓 Master Data Analysis and Skyrocket Your Career
Find Out the Secrets in Our Ultimate Guide! 💼
Independent samples t-test: This analysis compares the means of two independent groups.
Paired samples t-test: Compares the means of two related groups (e.g., before and after treatment).
Chi-squared test: Determines if there is a significant association, in a contingency table, between two categorical variables.
Analysis of Variance (ANOVA): Compares the means of three or more independent groups to determine whether significant differences exist.
Pearson’s Correlation Coefficient (Pearson’s r): Quantifies the strength and direction of a linear association between two continuous variables.
Simple Linear Regression: Evaluate whether a significant linear relationship exists between a predictor variable (X) and a continuous outcome variable (y).
Logistic Regression: Determines the relationship between one or more predictor variables (continuous or categorical) and a binary outcome variable (e.g., success or failure).
Levene’s Test: Tests the equality of variances between two or more groups, often used as an assumption checks for ANOVA.
Shapiro-Wilk Test: Assesses the null hypothesis that a data sample is drawn from a population with a normal distribution.
Hypothesis Test | Description | Application |
---|---|---|
Compares means of two independent groups | Comparing scores of two groups of students | |
Compares means of two related groups (e.g., before and after treatment) | Comparing weight loss before and after a diet program | |
Determines significant associations between two categorical variables in a contingency table | Analyzing the relationship between education and income | |
Compares means of three or more independent groups | Evaluating the impact of different teaching methods on test scores | |
Measures the strength and direction of a linear relationship between two continuous variables | Studying the correlation between height and weight | |
Determines a significant linear relationship between a predictor variable and an outcome variable | Predicting sales based on advertising budget | |
Determines the relationship between predictor variables and a binary outcome variable | Predicting the probability of loan default based on credit score | |
Tests the equality of variances between two or more groups | Checking the assumption of equal variances for ANOVA | |
Tests if a data sample is from a normally distributed population | Assessing normality assumption for parametric tests |
To interpret the hypothesis test results, compare the p-value to the chosen significance level.
If the p-value falls below the significance level, reject the null hypothesis and infer that a notable effect or difference exists.
Otherwise, fail to reject the null hypothesis, meaning there is insufficient evidence to support the alternative hypothesis.
In addition to understanding the basics of hypothesis tests, it’s crucial to consider other relevant information when interpreting the results.
For example, factors such as effect size, statistical power, and confidence intervals can provide valuable insights and help you make more informed decisions.
Effect size
The effect size represents a quantitative measurement of the strength or magnitude of the observed relationship or effect between variables. It aids in evaluating the practical significance of the results. A statistically significant outcome may not necessarily imply practical relevance. At the same time, a substantial effect size can suggest meaningful findings, even when statistical significance appears marginal.
Statistical power
The power of a test represents the likelihood of accurately rejecting the null hypothesis when it is incorrect. In other words, it’s the likelihood that the test will detect an effect when it exists. Factors affecting the power of a test include the sample size, effect size, and significance level. Enhanced power reduces the likelihood of making an error of Type II — failing to reject the null hypothesis when it ought to be rejected.
Confidence intervals
A confidence interval represents a range where the true population parameter is expected to be found with a specified confidence level (e.g., 95%). Confidence intervals provide additional context to hypothesis testing, helping to assess the estimate’s precision and offering a better understanding of the uncertainty surrounding the results.
By considering these additional aspects when interpreting the results of hypothesis tests, you can gain a more comprehensive understanding of the data and make more informed conclusions.
Hypothesis testing is an indispensable statistical tool for drawing meaningful inferences and making informed data-based decisions.
By comprehending the essential concepts such as null and alternative hypotheses, significance levels, p-values, and the distinction between parametric and non-parametric tests, you can proficiently apply hypothesis testing to a wide range of real-world situations.
Additionally, understanding the importance of effect sizes, statistical power, and confidence intervals will enhance your ability to interpret the results and make better decisions.
With many applications across various fields, including medicine, psychology, business, and environmental sciences, hypothesis testing is a versatile and valuable method for research and data analysis.
A comprehensive grasp of hypothesis testing techniques will enable professionals and researchers to strengthen their decision-making processes, optimize strategies, and deepen their understanding of the relationships between variables, leading to more impactful results and discoveries.
Access FREE samples now and master advanced techniques in data analysis, including optimal sample size determination and effective communication of results.
Don’t miss the chance to immerse yourself in Applied Statistics: Data Analysis and unlock your full potential in data-driven decision making.
Click the link to start exploring!
Connect with us on our social networks.
DAILY POSTS ON INSTAGRAM!
Similar posts.
Discover the hidden threat of p-hacking in data analysis. Uncover its effects, case studies, and strategies to combat it.
Explore the concept of Standard Deviation, a critical statistical measure, to understand data variability and when to apply it in data analysis.
Discover the essence of independence, the statistical correlation antonym, and its impact on data analysis through a concise overview.
We Have Already Presented A Didactic Explanation Of The P-Value, But Not That Precise. Now Learn An Accurate Definition For The P-Value!
Uncover the key differences between sample and population in statistics for accurate data interpretation and informed decision-making.
Discover the meaning of “when P value is less than 0.05,” its relevance to statistical significance, and how to interpret and understand its limitations.
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
If you're seeing this message, it means we're having trouble loading external resources on our website.
If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.
To log in and use all the features of Khan Academy, please enable JavaScript in your browser.
Unit 1: exploring categorical data, unit 2: exploring one-variable quantitative data: displaying and describing, unit 3: exploring one-variable quantitative data: summary statistics, unit 4: exploring one-variable quantitative data: percentiles, z-scores, and the normal distribution, unit 5: exploring two-variable quantitative data, unit 6: collecting data, unit 7: probability, unit 8: random variables and probability distributions, unit 9: sampling distributions, unit 10: inference for categorical data: proportions, unit 11: inference for quantitative data: means, unit 12: inference for categorical data: chi-square, unit 13: inference for quantitative data: slopes, unit 14: prepare for the 2022 ap®︎ statistics exam.
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
Get the world’s most fascinating discoveries delivered straight to your inbox.
Hot Tub of Despair: The deadly ocean pool that traps and pickles creatures that fall in
Enormous deposit of rare earth elements discovered in heart of ancient Norwegian volcano
Melatonin may stave off age-related vision loss, study hints
“extremely user friendly”
“truly amazing!”
“so easy to use!”
You want to analyze your data effortlessly? DATAtab makes it easy and online.
What do you want to calculate online? The online statistics calculator is simple and uncomplicated! Here you can find a list of all implemented methods!
Create your charts for your data directly online and uncomplicated. To do this, insert your data into the table under Charts and select which chart you want.
Statistics, as simple as never before..
DATAtab is a modern statistics software, with unique user-friendliness. Statistical analyses are done with just a few clicks, so DATAtab is perfect for statistics beginners and for professionals who want more flow in the user experience.
Directly in the browser, fully flexible. DATAtab works directly in your web browser. You have no installation and maintenance effort whatsoever. Wherever and whenever you want to use DATAtab, just go to the website and get started.
DATAtab offers you a wide range of statistical methods. We have selected the most central and best known statistical methods for you and do not overwhelm you with special cases.
All data that you insert and evaluate on DATAtab always remain on your end device. The data is not sent to any server or stored by us (not even temporarily). Furthermore, we do not pass on your data to third parties in order to analyze your user behavior.
In order to facilitate the introduction, DATAtab offers a large number of free tutorials with focused explanations in simple language. We explain the statistical background of the methods and give step-by-step explanations for performing the analyses in the statistics calculator.
DATAtab takes you by the hand in the world of statistics. When making statistical decisions, such as the choice of scale or measurement level or the selection of suitable methods, Auto-Assistants ensure that you get correct results quickly.
With DATAtab data visualization is fun! Here you can easily create meaningful charts that optimally illustrate your results.
DATAtab was primarily designed for people for whom statistics is new territory. Beginners are not overwhelmed with a lot of complicated options and checkboxes, but are encouraged to perform their analyses step by step.
DATAtab offers you the possibility to easily create an online survey, which you can then evaluate immediately with DATAtab.
DATAtab was designed for ease of use and is a compelling alternative to statistical programs such as SPSS and STATA. On datatab.net, data can be statistically evaluated directly online and very easily (e.g. t-test, regression, correlation etc.). DATAtab's goal is to make the world of statistical data analysis as simple as possible, no installation and easy to use. Of course, we would also be pleased if you take a look at our second project Statisty .
Descriptive statistics.
Here you can find out everything about location parameters and dispersion parameters and how you can describe and clearly present your data using characteristic values.
Here you will find everything about hypothesis testing: One sample t-test , Unpaired t-test , Paired t-test and Chi-square test . You will also find tutorials for non-parametric statistical procedures such as the Mann-Whitney u-Test and Wilcoxon-Test . mann-whitney-u-test and the Wilcoxon test
The regression provides information about the influence of one or more independent variables on the dependent variable. Here are simple explanations of linear regression and logistic regression .
Correlation analyses allow you to analyze the linear association between variables. Learn when to use Pearson correlation or Spearman rank correlation . With partial correlation , you can calculate the correlation between two variables to the exclusion of a third variable.
The partial correlation shows you the correlation between two variables to the exclusion of a third variable.
The Levene Test checks your data for variance equality. Thus, the levene test is used as a prerequisite test for many hypothesis tests .
The p-value is needed for every hypothesis test to be able to make a statement whether the null hypothesis is accepted or rejected.
DATAtab provides you with tables with distributions and helpful explanations of the distribution functions. These include the Table of t-distribution and the Table of chi-squared distribution
With a contingency table you can get an overview of two categorical variables in the statistics.
In an equivalence trial, the statistical test aims at showing that two treatments are not too different in characteristics and a non-inferiority trial wants to show that an experimental treatment is not worse than an established treatment.
If there is a clear cause-effect relationship between two variables, then we can speak of causality. Learn more about causality in our tutorial.
Multicollinearity is when two or more independent variables have a high correlation.
Learn how to calculate the effect size for the t-test for independent samples.
On DATAtab, Cohen's Kappa can be easily calculated online in the Cohen’s Kappa Calculator . there is also the Fleiss Kappa Calculator . Of course, the Cronbach's alpha can also be calculated in the Cronbach's Alpha Calculator .
Repeated measures ANOVA tests whether there are statistically significant differences in three or more dependent samples.
Cite DATAtab: DATAtab Team (2024). DATAtab: Online Statistics Calculator. DATAtab e.U. Graz, Austria. URL https://datatab.net
selected template will load here
This action is not available.
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)
\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)
( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)
\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)
\( \newcommand{\Span}{\mathrm{span}}\)
\( \newcommand{\id}{\mathrm{id}}\)
\( \newcommand{\kernel}{\mathrm{null}\,}\)
\( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\)
\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\)
\( \newcommand{\norm}[1]{\| #1 \|}\)
\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)
\( \newcommand{\vectorA}[1]{\vec{#1}} % arrow\)
\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}} % arrow\)
\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vectorC}[1]{\textbf{#1}} \)
\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)
\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)
\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)
To understand the process of a hypothesis tests, you need to first have an understanding of what a hypothesis is, which is an educated guess about a parameter. Once you have the hypothesis, you collect data and use the data to make a determination to see if there is enough evidence to show that the hypothesis is true. However, in hypothesis testing you actually assume something else is true, and then you look at your data to see how likely it is to get an event that your data demonstrates with that assumption. If the event is very unusual, then you might think that your assumption is actually false. If you are able to say this assumption is false, then your hypothesis must be true. This is known as a proof by contradiction. You assume the opposite of your hypothesis is true and show that it can’t be true. If this happens, then your hypothesis must be true. All hypothesis tests go through the same process. Once you have the process down, then the concept is much easier. It is easier to see the process by looking at an example. Concepts that are needed will be detailed in this example.
Example \(\PageIndex{1}\) basics of hypothesis testing
Suppose a manufacturer of the XJ35 battery claims the mean life of the battery is 500 days with a standard deviation of 25 days. You are the buyer of this battery and you think this claim is inflated. You would like to test your belief because without a good reason you can’t get out of your contract.
What do you do?
Well first, you should know what you are trying to measure. Define the random variable.
Let x = life of a XJ35 battery
Now you are not just trying to find different x values. You are trying to find what the true mean is. Since you are trying to find it, it must be unknown. You don’t think it is 500 days. If you did, you wouldn’t be doing any testing. The true mean, \(\mu\), is unknown. That means you should define that too.
Let \(\mu\)= mean life of a XJ35 battery
You may want to collect a sample. What kind of sample?
You could ask the manufacturers to give you batteries, but there is a chance that there could be some bias in the batteries they pick. To reduce the chance of bias, it is best to take a random sample.
How big should the sample be?
A sample of size 30 or more means that you can use the central limit theorem. Pick a sample of size 30.
Example \(\PageIndex{1}\) contains the data for the sample you collected:
491 | 485 | 503 | 492 | 282 | 490 |
489 | 495 | 497 | 487 | 493 | 480 |
483 | 504 | 501 | 486 | 478 | 492 |
482 | 502 | 485 | 503 | 497 | 500 |
488 | 475 | 478 | 490 | 487 | 486 |
Now what should you do? Looking at the data set, you see some of the times are above 500 and some are below. But looking at all of the numbers is too difficult. It might be helpful to calculate the mean for this sample.
The sample mean is \(\overline{x} = 490\) days. Looking at the sample mean, one might think that you are right. However, the standard deviation and the sample size also plays a role, so maybe you are wrong.
Before going any farther, it is time to formalize a few definitions.
You have a guess that the mean life of a battery is less than 500 days. This is opposed to what the manufacturer claims. There really are two hypotheses, which are just guesses here – the one that the manufacturer claims and the one that you believe. It is helpful to have names for them.
Definition \(\PageIndex{1}\)
Null Hypothesis : historical value, claim, or product specification. The symbol used is \(H_{o}\).
Definition \(\PageIndex{2}\)
Alternate Hypothesis : what you want to prove. This is what you want to accept as true when you reject the null hypothesis. There are two symbols that are commonly used for the alternative hypothesis: \(H_{A}\) or \(H_{I}\). The symbol \(H_{A}\) will be used in this book.
In general, the hypotheses look something like this:
\(H_{o} : \mu=\mu_{o}\)
\(H_{A} : \mu<\mu_{o}\)
where \(\mu_{o}\) just represents the value that the claim says the population mean is actually equal to.
Also, \(H_{A}\) can be less than, greater than, or not equal to.
For this problem:
\(H_{o} : \mu=500\) days, since the manufacturer says the mean life of a battery is 500 days.
\(H_{A} : \mu<500\) days, since you believe that the mean life of the battery is less than 500 days.
Now back to the mean. You have a sample mean of 490 days. Is this small enough to believe that you are right and the manufacturer is wrong? How small does it have to be?
If you calculated a sample mean of 235, you would definitely believe the population mean is less than 500. But even if you had a sample mean of 435 you would probably believe that the true mean was less than 500. What about 475? Or 483? There is some point where you would stop being so sure that the population mean is less than 500. That point separates the values of where you are sure or pretty sure that the mean is less than 500 from the area where you are not so sure. How do you find that point?
Well it depends on how much error you want to make. Of course you don’t want to make any errors, but unfortunately that is unavoidable in statistics. You need to figure out how much error you made with your sample. Take the sample mean, and find the probability of getting another sample mean less than it, assuming for the moment that the manufacturer is right. The idea behind this is that you want to know what is the chance that you could have come up with your sample mean even if the population mean really is 500 days.
You want to find \(P\left(\overline{x}<490 | H_{o} \text { is true }\right)=P(\overline{x}<490 | \mu=500)\)
To compute this probability, you need to know how the sample mean is distributed. Since the sample size is at least 30, then you know the sample mean is approximately normally distributed. Remember \(\mu_{\overline{x}}=\mu\) and \(\sigma_{\overline{x}}=\dfrac{\sigma}{\sqrt{n}}\)
A picture is always useful.
Before calculating the probability, it is useful to see how many standard deviations away from the mean the sample mean is. Using the formula for the z-score from chapter 6, you find
\(z=\dfrac{\overline{x}-\mu_{o}}{\sigma / \sqrt{n}}=\dfrac{490-500}{25 / \sqrt{30}}=-2.19\)
This sample mean is more than two standard deviations away from the mean. That seems pretty far, but you should look at the probability too.
On TI-83/84:
\(P(\overline{x}<490 | \mu=500)=\text { normalcdf }(-1 E 99,490,500,25 \div \sqrt{30}) \approx 0.0142\)
\(P(\overline{x}<490 \mu=500)=\text { pnorm }(490,500,25 / \operatorname{sqrt}(30)) \approx 0.0142\)
There is a 1.42% chance that you could find a sample mean less than 490 when the population mean is 500 days. This is really small, so the chances are that the assumption that the population mean is 500 days is wrong, and you can reject the manufacturer’s claim. But how do you quantify really small? Is 5% or 10% or 15% really small? How do you decide?
Before you answer that question, a couple more definitions are needed.
Definition \(\PageIndex{3}\)
Test Statistic : \(z=\dfrac{\overline{x}-\mu_{o}}{\sigma / \sqrt{n}}\) since it is calculated as part of the testing of the hypothesis.
Definition \(\PageIndex{4}\)
p – value : probability that the test statistic will take on more extreme values than the observed test statistic, given that the null hypothesis is true. It is the probability that was calculated above.
Now, how small is small enough? To answer that, you really want to know the types of errors you can make.
There are actually only two errors that can be made. The first error is if you say that \(H_{o}\) is false, when in fact it is true. This means you reject \(H_{o}\) when \(H_{o}\) was true. The second error is if you say that \(H_{o}\) is true, when in fact it is false. This means you fail to reject \(H_{o}\) when \(H_{o}\) is false. The following table organizes this for you:
Type of errors:
\(H_{o}\) true | \(H_{o}\) false | |
Reject \(H_{o}\) | Type 1 error | No error |
Fail to reject \(H_{o}\) | No error | Type II error |
Definition \(\PageIndex{5}\)
Type I Error is rejecting \(H_{o}\) when \(H_{o}\) is true, and
Definition \(\PageIndex{6}\)
Type II Error is failing to reject \(H_{o}\) when \(H_{o}\) is false.
Since these are the errors, then one can define the probabilities attached to each error.
Definition \(\PageIndex{7}\)
\(\alpha\) = P(type I error) = P(rejecting \(H_{o} / H_{o}\) is true)
Definition \(\PageIndex{8}\)
\(\beta\) = P(type II error) = P(failing to reject \(H_{o} / H_{o}\) is false)
\(\alpha\) is also called the level of significance .
Another common concept that is used is Power = \(1-\beta \).
Now there is a relationship between \(\alpha\) and \(\beta\). They are not complements of each other. How are they related?
If \(\alpha\) increases that means the chances of making a type I error will increase. It is more likely that a type I error will occur. It makes sense that you are less likely to make type II errors, only because you will be rejecting \(H_{o}\) more often. You will be failing to reject \(H_{o}\) less, and therefore, the chance of making a type II error will decrease. Thus, as \(\alpha\) increases, \(\beta\) will decrease, and vice versa. That makes them seem like complements, but they aren’t complements. What gives? Consider one more factor – sample size.
Consider if you have a larger sample that is representative of the population, then it makes sense that you have more accuracy then with a smaller sample. Think of it this way, which would you trust more, a sample mean of 490 if you had a sample size of 35 or sample size of 350 (assuming a representative sample)? Of course the 350 because there are more data points and so more accuracy. If you are more accurate, then there is less chance that you will make any error. By increasing the sample size of a representative sample, you decrease both \(\alpha\) and \(\beta\).
Summary of all of this:
Now how do you find \(\alpha\) and \(\beta\)? Well \(\alpha\) is actually chosen. There are only three values that are usually picked for \(\alpha\): 0.01, 0.05, and 0.10. \(\beta\) is very difficult to find, so usually it isn’t found. If you want to make sure it is small you take as large of a sample as you can afford provided it is a representative sample. This is one use of the Power. You want \(\beta\) to be small and the Power of the test is large. The Power word sounds good.
Which pick of \(\alpha\) do you pick? Well that depends on what you are working on. Remember in this example you are the buyer who is trying to get out of a contract to buy these batteries. If you create a type I error, you said that the batteries are bad when they aren’t, most likely the manufacturer will sue you. You want to avoid this. You might pick \(\alpha\) to be 0.01. This way you have a small chance of making a type I error. Of course this means you have more of a chance of making a type II error. No big deal right? What if the batteries are used in pacemakers and you tell the person that their pacemaker’s batteries are good for 500 days when they actually last less, that might be bad. If you make a type II error, you say that the batteries do last 500 days when they last less, then you have the possibility of killing someone. You certainly do not want to do this. In this case you might want to pick \(\alpha\) as 0.10. If both errors are equally bad, then pick \(\alpha\) as 0.05.
The above discussion is why the choice of \(\alpha\) depends on what you are researching. As the researcher, you are the one that needs to decide what \(\alpha\) level to use based on your analysis of the consequences of making each error is.
If a type I error is really bad, then pick \(\alpha\) = 0.01.
If a type II error is really bad, then pick \(\alpha\) = 0.10
If neither error is bad, or both are equally bad, then pick \(\alpha\) = 0.05
The main thing is to always pick the \(\alpha\) before you collect the data and start the test.
The above discussion was long, but it is really important information. If you don’t know what the errors of the test are about, then there really is no point in making conclusions with the tests. Make sure you understand what the two errors are and what the probabilities are for them.
Now it is time to go back to the example and put this all together. This is the basic structure of testing a hypothesis, usually called a hypothesis test. Since this one has a test statistic involving z, it is also called a z-test. And since there is only one sample, it is usually called a one-sample z-test.
Example \(\PageIndex{2}\) battery example revisited
1. x = life of battery
\(\mu\) = mean life of a XJ35 battery
2. \(H_{o} : \mu=500\) days
\(H_{A} : \mu<500\) days
\(\alpha = 0.10\) (from above discussion about consequences)
3. Every hypothesis has some assumptions that be met to make sure that the results of the hypothesis are valid. The assumptions are different for each test. This test has the following assumptions.
4. The test statistic depends on how many samples there are, what parameter you are testing, and assumptions that need to be checked. In this case, there is one sample and you are testing the mean. The assumptions were checked above.
Sample statistic:
\(\overline{x} = 490\)
Test statistic:
Using TI-83/84:
\(P(\overline{x}<490 | \mu=500)=\text { normalcdf }(-1 \mathrm{E} 99,490,500,25 / \sqrt{30}) \approx 0.0142\)
\(P(\overline{x}<490 | \mu=500)=\operatorname{pnorm}(490,500,25 / \operatorname{sqrt}(30)) \approx 0.0142\)
5. Now what? Well, this p-value is 0.0142. This is a lot smaller than the amount of error you would accept in the problem -\(\alpha\) = 0.10. That means that finding a sample mean less than 490 days is unusual to happen if \(H_{o}\) is true. This should make you think that \(H_{o}\) is not true. You should reject \(H_{o}\).
In fact, in general:
Reject \(H_{o}\) if the p-value < \(\alpha\) and
Fail to reject \(H_{o}\) if the p-value \(\geq \alpha\).
6. Since you rejected \(H_{o}\), what does this mean in the real world? That is what goes in the interpretation. Since you rejected the claim by the manufacturer that the mean life of the batteries is 500 days, then you now can believe that your hypothesis was correct. In other words, there is enough evidence to show that the mean life of the battery is less than 500 days.
Now that you know that the batteries last less than 500 days, should you cancel the contract? Statistically, there is evidence that the batteries do not last as long as the manufacturer says they should. However, based on this sample there are only ten days less on average that the batteries last. There may not be practical significance in this case. Ten days do not seem like a large difference. In reality, if the batteries are used in pacemakers, then you would probably tell the patient to have the batteries replaced every year. You have a large buffer whether the batteries last 490 days or 500 days. It seems that it might not be worth it to break the contract over ten days. What if the 10 days was practically significant? Are there any other things you should consider? You might look at the business relationship with the manufacturer. You might also look at how much it would cost to find a new manufacturer. These are also questions to consider before making any changes. What this discussion should show you is that just because a hypothesis has statistical significance does not mean it has practical significance. The hypothesis test is just one part of a research process. There are other pieces that you need to consider.
That’s it. That is what a hypothesis test looks like. All hypothesis tests are done with the same six steps. Those general six steps are outlined below.
Sorry, one more concept about the conclusion and interpretation. First, the conclusion is that you reject \(H_{o}\) or you fail to reject \(H_{o}\). Why was it said like this? It is because you never accept the null hypothesis. If you wanted to accept the null hypothesis, then why do the test in the first place? In the interpretation, you either have enough evidence to show \(H_{A}\) is true, or you do not have enough evidence to show \(H_{A}\) is true. You wouldn’t want to go to all this work and then find out you wanted to accept the claim. Why go through the trouble? You always want to show that the alternative hypothesis is true. Sometimes you can do that and sometimes you can’t. It doesn’t mean you proved the null hypothesis; it just means you can’t prove the alternative hypothesis. Here is an example to demonstrate this.
Example \(\PageIndex{3}\) conclusion in hypothesis tests
In the U.S. court system a jury trial could be set up as a hypothesis test. To really help you see how this works, let’s use OJ Simpson as an example. In the court system, a person is presumed innocent until he/she is proven guilty, and this is your null hypothesis. OJ Simpson was a football player in the 1970s. In 1994 his ex-wife and her friend were killed. OJ Simpson was accused of the crime, and in 1995 the case was tried. The prosecutors wanted to prove OJ was guilty of killing his wife and her friend, and that is the alternative hypothesis
\(H_{0}\): OJ is innocent of killing his wife and her friend
\(H_{A}\): OJ is guilty of killing his wife and her friend
In this case, a verdict of not guilty was given. That does not mean that he is innocent of this crime. It means there was not enough evidence to prove he was guilty. Many people believe that OJ was guilty of this crime, but the jury did not feel that the evidence presented was enough to show there was guilt. The verdict in a jury trial is always guilty or not guilty!
The same is true in a hypothesis test. There is either enough or not enough evidence to show that alternative hypothesis. It is not that you proved the null hypothesis true.
When identifying hypothesis, it is important to state your random variable and the appropriate parameter you want to make a decision about. If count something, then the random variable is the number of whatever you counted. The parameter is the proportion of what you counted. If the random variable is something you measured, then the parameter is the mean of what you measured. (Note: there are other parameters you can calculate, and some analysis of those will be presented in later chapters.)
Example \(\PageIndex{4}\) stating hypotheses
Identify the hypotheses necessary to test the following statements:
a. x = salary of teacher
\(\mu\) = mean salary of teacher
The guess is that \(\mu>\$ 30,000\) and that is the alternative hypothesis.
The null hypothesis has the same parameter and number with an equal sign.
\(\begin{array}{l}{H_{0} : \mu=\$ 30,000} \\ {H_{A} : \mu>\$ 30,000}\end{array}\)
b. x = number od students who like math
p = proportion of students who like math
The guess is that p < 0.10 and that is the alternative hypothesis.
\(\begin{array}{l}{H_{0} : p=0.10} \\ {H_{A} : p<0.10}\end{array}\)
c. x = age of students in this class
\(\mu\) = mean age of students in this class
The guess is that \(\mu \neq 21\) and that is the alternative hypothesis.
\(\begin{array}{c}{H_{0} : \mu=21} \\ {H_{A} : \mu \neq 21}\end{array}\)
Example \(\PageIndex{5}\) Stating Type I and II Errors and Picking Level of Significance
a. x = time to first berry for YumYum Berry plant
\(\mu\) = mean time to first berry for YumYum Berry plant
\(\begin{array}{l}{H_{0} : \mu=90} \\ {H_{A} : \mu>90}\end{array}\)
Type I Error: If the corporation does a type I error, then they will say that the plants take longer to produce than 90 days when they don’t. They probably will not want to market the plants if they think they will take longer. They will not market them even though in reality the plants do produce in 90 days. They may have loss of future earnings, but that is all.
Type II error: The corporation do not say that the plants take longer then 90 days to produce when they do take longer. Most likely they will market the plants. The plants will take longer, and so customers might get upset and then the company would get a bad reputation. This would be really bad for the company.
Level of significance: It appears that the corporation would not want to make a type II error. Pick a 10% level of significance, \(\alpha = 0.10\).
b. x = number of Aboriginal prisoners who have died
p = proportion of Aboriginal prisoners who have died
\(\begin{array}{l}{H_{o} : p=0.27 \%} \\ {H_{A} : p>0.27 \%}\end{array}\)
Type I error: Rejecting that the proportion of Aboriginal prisoners who died was 0.27%, when in fact it was 0.27%. This would mean you would say there is a problem when there isn’t one. You could anger the Aboriginal community, and spend time and energy researching something that isn’t a problem.
Type II error: Failing to reject that the proportion of Aboriginal prisoners who died was 0.27%, when in fact it is higher than 0.27%. This would mean that you wouldn’t think there was a problem with Aboriginal prisoners dying when there really is a problem. You risk causing deaths when there could be a way to avoid them.
Level of significance: It appears that both errors may be issues in this case. You wouldn’t want to anger the Aboriginal community when there isn’t an issue, and you wouldn’t want people to die when there may be a way to stop it. It may be best to pick a 5% level of significance, \(\alpha = 0.05\).
Hypothesis testing is really easy if you follow the same recipe every time. The only differences in the various problems are the assumptions of the test and the test statistic you calculate so you can find the p-value. Do the same steps, in the same order, with the same words, every time and these problems become very easy.
Exercise \(\PageIndex{1}\)
For the problems in this section, a question is being asked. This is to help you understand what the hypotheses are. You are not to run any hypothesis tests and come up with any conclusions in this section.
1. \(H_{o} : p=0.11, H_{A} : p>0.11\)
3. \(H_{o} : \mu=4.87 \text { metric tons per capita, } H_{A} : \mu<4.87 \text { metric tons per capita }\)
5. See solutions
7. See solutions
Help | Advanced Search
Title: federated nonparametric hypothesis testing with differential privacy constraints: optimal rates and adaptive tests.
Abstract: Federated learning has attracted significant recent attention due to its applicability across a wide range of settings where data is collected and analyzed across disparate locations. In this paper, we study federated nonparametric goodness-of-fit testing in the white-noise-with-drift model under distributed differential privacy (DP) constraints. We first establish matching lower and upper bounds, up to a logarithmic factor, on the minimax separation rate. This optimal rate serves as a benchmark for the difficulty of the testing problem, factoring in model characteristics such as the number of observations, noise level, and regularity of the signal class, along with the strictness of the $(\epsilon,\delta)$-DP requirement. The results demonstrate interesting and novel phase transition phenomena. Furthermore, the results reveal an interesting phenomenon that distributed one-shot protocols with access to shared randomness outperform those without access to shared randomness. We also construct a data-driven testing procedure that possesses the ability to adapt to an unknown regularity parameter over a large collection of function classes with minimal additional cost, all while maintaining adherence to the same set of DP constraints.
Comments: | 77 pages total; consisting of a main article (28 pages) and supplement (49 pages) |
Subjects: | Statistics Theory (math.ST); Machine Learning (cs.LG); Machine Learning (stat.ML) |
classes: | 62G10, 62C20, 68P27, 62F30 |
Cite as: | [math.ST] |
(or [math.ST] for this version) | |
Focus to learn more arXiv-issued DOI via DataCite |
Access paper:.
Code, data and media associated with this article, recommenders and search tools.
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .
IMAGES
VIDEO
COMMENTS
Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Published on November 8, 2019 by Rebecca Bevans.Revised on June 22, 2023. Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics.It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
A hypothesis test is a formal procedure to check if a hypothesis is true or not. Examples of claims that can be checked: The average height of people in Denmark is more than 170 cm. The share of left handed people in Australia is not 10%. The average income of dentists is less the average income of lawyers.
Hypothesis testing is a form of inferential statistics that allows us to draw conclusions about an entire population based on a representative sample. You gain tremendous benefits by working with a sample. In most cases, it is simply impossible to observe the entire population to understand its properties.
The above image shows a table with some of the most common test statistics and their corresponding tests or models.. A statistical hypothesis test is a method of statistical inference used to decide whether the data sufficiently support a particular hypothesis. A statistical hypothesis test typically involves a calculation of a test statistic.Then a decision is made, either by comparing the ...
A hypothesis test consists of five steps: 1. State the hypotheses. State the null and alternative hypotheses. These two hypotheses need to be mutually exclusive, so if one is true then the other must be false. 2. Determine a significance level to use for the hypothesis. Decide on a significance level.
In hypothesis testing, the goal is to see if there is sufficient statistical evidence to reject a presumed null hypothesis in favor of a conjectured alternative hypothesis.The null hypothesis is usually denoted \(H_0\) while the alternative hypothesis is usually denoted \(H_1\). An hypothesis test is a statistical decision; the conclusion will either be to reject the null hypothesis in favor ...
Hypothesis testing involves five key steps, each critical to validating a research hypothesis using statistical methods: Formulate the Hypotheses: Write your research hypotheses as a null hypothesis (H 0) and an alternative hypothesis (H A ). Data Collection: Gather data specifically aimed at testing the hypothesis.
Unit 12: Significance tests (hypothesis testing) Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. Learn how to conduct significance tests and calculate p-values to see how likely a sample result is to occur by random chance. You'll also see how we use p-values ...
This is also the case with hypothesis testing: even if we fail to reject the null hypothesis, we typically do not accept the null hypothesis as true. Failing to find strong evidence for the alternative hypothesis is not equivalent to accepting the null hypothesis. 17 H 0: The average cost is $650 per month, μ = $650.
S.3 Hypothesis Testing. In reviewing hypothesis tests, we start first with the general idea. Then, we keep returning to the basic procedures of hypothesis testing, each time adding a little more detail. The general idea of hypothesis testing involves: Making an initial assumption. Collecting evidence (data).
Step 7: Based on steps 5 and 6, draw a conclusion about H0. If the F\calculated from the data is larger than the Fα, then you are in the rejection region and you can reject the null hypothesis with (1 − α) level of confidence. Note that modern statistical software condenses steps 6 and 7 by providing a p -value.
8.1 Inferential Statistics and Hypothesis Testing 8.2 Four Steps to Hypothesis Testing 8.3 Hypothesis Testing and Sampling Distributions 8.4 Making a Decision: 8.5 Testing a Research Using the z Test 8.6 Research in Focus: Directional Versus Nondirectional Tests 8.7 Measuring the Size of an Effect: Cohen's d 8.8 Effect Size, Power, and
Hypothesis testing is a formal procedure for investigating our ideas about the world. It allows you to statistically test your predictions. ... Test statistics | Definition, Interpretation, and Examples The test statistic is a number, calculated from a statistical test, used to find if your data could have occurred under the null hypothesis. 254.
Photo from StepUp Analytics. Hypothesis testing is a method of statistical inference that considers the null hypothesis H₀ vs. the alternative hypothesis Ha, where we are typically looking to assess evidence against H₀. Such a test is used to compare data sets against one another, or compare a data set against some external standard. The former being a two sample test (independent or ...
The test statistic is a number calculated from a statistical test of a hypothesis. It shows how closely your observed data match the distribution expected under the null hypothesis of that statistical test. The test statistic is used to calculate the p value of your results, helping to decide whether to reject your null hypothesis.
Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence.
What is Hypothesis Testing? Hypothesis testing in statistics is a tool that is used to make inferences about the population data. It is also used to check if the results of an experiment are valid. What is the z Test in Hypothesis Testing? The z test in hypothesis testing is used to find the z test statistic for normally distributed data. The z ...
This tests whether the population parameter is equal to, versus less than, some specific value. Ho: μ = 12 vs. H1: μ < 12. The critical region is in the left tail and the critical value is a negative value that defines the rejection zone. Figure 3.1.3 3.1. 3: The rejection zone for a left-sided hypothesis test.
HYPOTHESIS TESTING. A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the "alternate" hypothesis, and the opposite ...
Introduction to Hypotheses Tests. Hypothesis testing is a statistical tool used to make decisions based on data. It involves making assumptions about a population parameter and testing its validity using a population sample. Hypothesis tests help us draw conclusions and make informed decisions in various fields like business, research, and science.
Statistics: Hypothesis Testing . A hypothesis is a claim made about a population. A hypothesis test uses sample data to test the validity of the claim. This handout will define the basic elements of hypothesis testing and provide the steps to perform hypothesis tests using the P-value method and the critical value
The hypothesis testing is a statistical method that allows to formulate and evaluate an hypothesis about the population based on sample data. So, it is a form of inferential statistics. This process starts with a hypothesis of the population parameters, also called null hypothesis, that needs to be tested, while the alternative hypothesis (H1 ...
Unit 14: Prepare for the 2022 AP®︎ Statistics Exam. Mastery unavailable. Prepare for the exam. Up next for you: Course challenge Test your knowledge of the skills in this course. Start Course challenge.
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 ...
Hypothesis Testing Step 1: State the Hypotheses. In all three examples, our aim is to decide between two opposing points of view, Claim 1 and Claim 2. In hypothesis testing, Claim 1 is called the null hypothesis (denoted " Ho "), and Claim 2 plays the role of the alternative hypothesis (denoted " Ha ").
DATAtab is a modern statistics software, with unique user-friendliness. Statistical analyses are done with just a few clicks, so DATAtab is perfect for statistics beginners and for professionals who want more flow in the user experience. ... Here you will find everything about hypothesis testing: One sample t-test, Unpaired t-test, Paired t ...
Hypothesis Testing • … "one of the most important tools for the application of statistics to business problems" • We are interested in making claims about a population from a sample of data • Those claims are usually stated as hypotheses and we use hypothesis testing to determine if a claim (hypothesis) is supported or not with statistical evidence EC255: Chapter 9 5
Test Statistic: z = x¯¯¯ −μo σ/ n−−√ z = x ¯ − μ o σ / n since it is calculated as part of the testing of the hypothesis. Definition 7.1.4 7.1. 4. p - value: probability that the test statistic will take on more extreme values than the observed test statistic, given that the null hypothesis is true.
Federated learning has attracted significant recent attention due to its applicability across a wide range of settings where data is collected and analyzed across disparate locations. In this paper, we study federated nonparametric goodness-of-fit testing in the white-noise-with-drift model under distributed differential privacy (DP) constraints. We first establish matching lower and upper ...