“second variable, we find that Z = 2.103, p = .035. This value is larger than that obtained by the parametric test, p = .019.21 SUMMARY When analysts need to determine whether two groups have different means of a continuous variable, the t-test is the tool of choice. This situation arises, for example, when analysts compare measurements at two points in time or the responses of two different groups. There are three common t-tests, involving independent samples, dependent (paired) samples, and the one-sample t-test. T-tests are parametric tests, which means that variables in these tests must meet certain assumptions, notably that they are normally distributed. The requirement of normally distributed variables follows from how parametric tests make inferences. Specifically, t-tests have four assumptions: One variable is continuous, and the other variable is dichotomous. The two distributions have equal variances. The observations are independent. The two distributions are normally distributed. The assumption of homogeneous variances does not apply to dependent-samples and one-sample t-tests because both are based on only a single variable for testing significance. When assumptions of normality are not met, variable transformation may be used. The search for alternative ways for dealing with normality problems may lead analysts to consider nonparametric alternatives. The chief advantage of nonparametric tests is that they do not require continuous variables to be normally distributed. The chief disadvantage is that they yield higher levels of statistical significance, making it less likely that the null hypothesis may be rejected. A nonparametric alternative for the independent-samples t-test is the Mann-Whitney test, and the nonparametric alternative for the dependent-samples t-test is the Wilcoxon”