3. Enter data Show
4. View results A t test compares the means of two groups. There are several types of two sample t tests and this calculator focuses on the three most common: unpaired, welch's, and paired t tests. What is a t test?A t test is used to measure the difference between exactly two means. Its focus is on the same numeric data variable rather than counts or correlations between multiple variables. If you are taking the average of a sample of measurements, t tests are the most commonly used method to evaluate that data. It is particularly useful for small samples of less than 30 observations. For example, you might compare whether systolic blood pressure differs between a control and treated group, between men and women, or any other two groups. This calculator uses a two-sample t test, which compares two datasets to see if their means are statistically different. That is different from a one sample t test, which compares the mean of your sample to some proposed theoretical value. The most general formula for a t test is composed of two means (M1 and M2) and the overall standard error (SE) of the two samples: See our video on How to Perform a Two-sample t test for an intuitive explanation of t tests and an example. How to use the t test calculator
Performing t tests? We can help.Sign up for more information on how to perform t tests and other common statistical analyses. Common t test confusionIn addition to the number of t test options, t tests are often confused with completely different techniques as well. Here's how to keep them all straight. Correlation and regression are used to measure how much two factors move together. While t tests are part of regression analysis, they are focused on only one factor by comparing means in different samples. ANOVA is used for comparing means across three or more total groups. In contrast, t tests compare means between exactly two groups. Finally, contingency tables compare counts of observations within groups rather than a calculated average. Since t tests compare means of continuous variable between groups, contingency tables use methods such as chi square instead of t tests. Assumptions of t testsBecause there are several versions of t tests, it's important to check the assumptions to figure out which is best suited for your project. Here are our analysis checklists for unpaired t tests and paired t tests, which are the two most common. These (and the ultimate guide to t tests) go into detail on the basic assumptions underlying any t test:
Interpreting resultsThe three different options for t tests have slightly different interpretations, but they all hinge on hypothesis testing and P values. You need to select a significance threshold for your P value (often 0.05) before doing the test. While P values can be easy to misinterpret, they are the most commonly used method to evaluate whether there is evidence of a difference between the sample of data collected and the null hypothesis. Once you have run the correct t test, look at the resulting P value. If the test result is less than your threshold, you have enough evidence to conclude that the data are significantly different. If the test result is larger or equal to your threshold, you cannot conclude that there is a difference. However, you cannot conclude that there was definitively no difference either. It's possible that a dataset with more observations would have resulted in a different conclusion. Depending on the test you run, you may see other statistics that were used to calculate the P value, including the mean difference, t statistic, degrees of freedom, and standard error. The confidence interval and a review of your dataset is given as well on the results page. Graphing t testsThis calculator does not provide a chart or graph of t tests, however, graphing is an important part of analysis because it can help explain the results of the t test and highlight any potential outliers. See our Prism guide for some graphing tips for both unpaired and paired t tests. Prism is built for customized, publication quality graphics and charts. For t tests we recommend simply plotting the datapoints themselves and the mean, or an estimation plot. Another popular approach is to use a violin plot, like those available in Prism. For more informationOur ultimate guide to t tests includes examples, links, and intuitive explanations on the subject. It is quite simply the best place to start if you're looking for more about t tests! If you enjoyed this calculator, you will love using Prism for analysis. Take a free 30-day trial to do more with your data, such as:
Check out our video on how to perform a t test in Prism, for an example from start to finish! Remember, this page is just for two sample t tests. If you only have one sample, you need to use this calculator instead. We Recommend:Analyze, graph and present your scientific work easily with GraphPad Prism. No coding required. What is the difference between tThe t-test is a method that determines whether two populations are statistically different from each other, whereas ANOVA determines whether three or more populations are statistically different from each other.
What is the main difference between tThe Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups.
What do the tAssumptions. Both the t-test and the ANOVA have the same assumptions: normality and homogeneity of variance. The normality assumptions can be assessed with a Shapiro Wilks test or by a Q-Q scatterplot. The homogeneity of variance test can be assessed with the Levene's test.
Which test is used for test the differences between the variance of a sample?An F-test (Snedecor and Cochran, 1983) is used to test if the variances of two populations are equal. This test can be a two-tailed test or a one-tailed test. The two-tailed version tests against the alternative that the variances are not equal.
|