| Summary: | The classical procedures of comparing two groups, such as t-test are, usually restricted with the assumptions of normality and equal variances.When these assumptions are violated, the rates of the Type I errors of the independent samples t-test are affected, particularly when the sample sizes are small.In this situation, the bootstrap procedure has an advantage over the parametric.In this study, the performances of the bootstrap procedure and the independent sample 1-test were investigated. The investigation focused on the power of both the test procedures to compare the two groups under different design specifications for normal and chi-square distributions.The results showed that the bootstrap procedure has 21 slight edge over the conventional
t-test in term of the rate of achieving the benchmark level for both the distributions.In fact, the bootstrap
procedure consistently outperformed the convention t-test across all the combinations of the test conditions.
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