Abstract

When comparing two independent groups, psychology researchers commonly use Student’s t-tests. Assumptions of normality and homogeneity of variance underlie this test. More often than not, when these conditions are not met, Student’s t-test can be severely biased and lead to invalid statistical inferences. Moreover, we argue that the assumption of equal variances will seldom hold in psychological research, and choosing between Student’s t-test and Welch’s t-test based on the outcomes of a test of the equality of variances often fails to provide an appropriate answer. We show that the Welch’s t-test provides a better control of Type 1 error rates when the assumption of homogeneity of variance is not met, and it loses little robustness compared to Student’s t-test when the assumptions are met. We argue that Welch’s t-test should be used as a default strategy.

Highlights

  • When comparing two independent groups, psychology researchers commonly use Student’s t-tests

  • If variances are not equal across groups and the sample sizes differ across independent groups, Student’s t-test can be severely biased and lead to invalid statistical inferences (Erceg-Hurn & Mirosevich, 2008)

  • When the larger variance is associated with the larger sample size, there is a decrease in the nominal Type 1 error rate (Nimon, 2012; Overall, Atlas, & Gibson, 1995)

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Summary

RESEARCH ARTICLE

Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test. When comparing two independent groups, psychology researchers commonly use Student’s t-tests. There are different types of t-tests, such as Student’s t-test, Welch’s t-test, Yuen’s t-test, and a bootstrapped t-test These variations differ in the underlying assumptions about whether data is normally distributed and whether variances in both groups are equal (see, e.g., Rasch, Kubinger, & Moder, 2011; Yuen, 1974). If variances are not equal across groups and the sample sizes differ across independent groups, Student’s t-test can be severely biased and lead to invalid statistical inferences (Erceg-Hurn & Mirosevich, 2008).. We will first discuss why we need a default test and why a two-step procedure where researchers decide whether or not to use Welch’s t-test based on a check of the assumption of normality and equal variances is undesirable. Testing the equality of variances before deciding which t-test is performed is problematic for several reasons, which will be explained after having described some of the most widely used tests of equality of variances

Different Ways to Test for Equal Variances
The Mathematical Differences Between
Previous work by many researchers has shown that
Unequal variances
Findings
Conclusion
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