Another form of the test, known as Welch’s t-test, does not assume equal variances.Īs an aside, thanks to the central limit theorem, you can safely use t-tests to analyze nonnormal data when have ~20 or more observations per group. However, the conventional t-test also assumes the standard deviations/variances for both groups are equal. All t-tests assume you obtained data from normally distributed populations. Variances and the closely related standard deviation are measures of variability. One that assumes equal variances and the other that assumes unequal variances. You’ll notice that Excel has two forms of the two-sample t-test. The standard form tests the following hypotheses: To learn more about unstandardized and standardized effect sizes, read my post about Effect Sizes in Statistics. Effect sizes help you understand how important the findings are in a practical sense. Cohen’s d is the corresponding standardized effect size and it’s appropriate to report in some cases. Statisticians consider differences between group means to be an unstandardized effect size because these values indicate the strength of the relationship using values that retain the natural units of the dependent variable. In other words, each group contains a unique set of people or items. For example, do students who learn using Method A have a different mean score than those who learn using Method B? This form of the test uses independent samples. Two-sample t-tests compare the means of precisely two groups-no more and no less! Typically, you perform this test to determine whether two population means are different.

How to find data analysis tool in excel