A MORE REALISTIC LOOK AT THE ROBUSTNESS AND TYPE-II ERROR PROPERTIES OF THE T-TEST TO DEPARTURES FROM POPULATION NORMALITY

The Type I and II error properties of the t test were evaluated by means of a Monte Carlo study that sampled 8 real distribution shapes identified by Micceri (1986, 1989) as being representative of types encountered in psychology and education research. Results showed the independent-samples t tests to be reasonably robust to Type I error when (a) sample sizes are equal, (b) sample sizes are fairly large, and (c) tests are two-tailed rather than one-tailed. Nonrobust results were obtained primarily under distributions with extreme skew. The t test was robust to Type II error under these nonnormal distributions, but researchers should not overlook robust nonparametric competitors that are often more powerful than the t test when its underlying assumptions are violated.