What is the primary assumption of parametric tests?

What is the primary assumption of parametric tests?

• A. Normal distribution
• B. Equal sample sizes
• C. Data must be nominal
• D. There must be no missing data

Answer: (A)

Parametric tests rely on the data coming from a population that is normally distributed. This normality underpins the sampling distributions of the test statistics (such as the t statistic or the F statistic), which in turn allows us to calculate accurate p-values and confidence intervals. Because of this, the data are typically assumed to be on an interval or ratio scale, and the model often looks at means and variances rather than simple ranks or categories. Equal sample sizes aren’t a requirement for these tests, though severe imbalance can affect statistical power and other specifics of the analysis. Missing data isn’t a fundamental assumption either—researchers handle it by excluding cases or using imputation or other methods. If normality is clearly violated, nonparametric tests, which do not assume a normal distribution, become more appropriate.

Parametric tests rely on the data coming from a population that is normally distributed. This normality underpins the sampling distributions of the test statistics (such as the t statistic or the F statistic), which in turn allows us to calculate accurate p-values and confidence intervals. Because of this, the data are typically assumed to be on an interval or ratio scale, and the model often looks at means and variances rather than simple ranks or categories.

Equal sample sizes aren’t a requirement for these tests, though severe imbalance can affect statistical power and other specifics of the analysis. Missing data isn’t a fundamental assumption either—researchers handle it by excluding cases or using imputation or other methods. If normality is clearly violated, nonparametric tests, which do not assume a normal distribution, become more appropriate.