While t tests are part of regression analysis, they are focused on only one factor by comparing means in different samples.ĪNOVA is used for comparing means across three or more total groups. Here's how to keep them all straight.Ĭorrelation and regression are used to measure how much two factors move together. In addition to the number of t test options, t tests are often confused with completely different techniques as well. All options will perform a two-tailed test. Notice not all options are available if you enter means only.Įnter data for the test, based on the format you chose in Step 1.Ĭlick Calculate Now and View the results. Use our Ultimate Guide to t tests if you are unsure which is appropriate, as it includes a section on 'How do I know which t test to use?'. If you have already calculated these summary statistics, the latter options will save you time.Ĭhoose a test from the three options: Unpaired t test, Welch's unpaired t test, or Paired t test. The last two are for entering the means for each group, along with the number of observations (N) and either the standard error of that mean (SEM) or standard deviation of the dataset (SD) standard error.

The first two options are for entering your data points themselves, either manually or by copy & paste. This will change how section 3 on the page looks. See our video on How to Perform a Two-sample t test for an intuitive explanation of t tests and an example.Ĭhoose your data entry format. 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: That is different from a one sample t test, which compares the mean of your sample to some proposed theoretical value.

This calculator uses a two-sample t test, which compares two datasets to see if their means are statistically different. 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. It is particularly useful for small samples of less than 30 observations. If you are taking the average of a sample of measurements, t tests are the most commonly used method to evaluate that data. Its focus is on the same numeric data variable rather than counts or correlations between multiple variables. A t test is used to measure the difference between exactly two means.

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