Why are parametric tests considered robust?

Parametric tests are considered robust mainly because their accuracy and validity remain relatively unaffected by violations of certain assumptions, such as normality or homogeneity of variance, as long as these violations are not severe. This robustness implies that parametric methods can still yield reliable results even when the data do not perfectly meet the ideal conditions typically postulated for their application.

The strength of parametric tests lies in their power to detect real effects when the conditions are somewhat violated, making them versatile tools in a researcher's analytic toolkit. For instance, in many practical research scenarios, data tend to have some imperfections (e.g., slight deviations from a normal distribution), yet parametric tests can still perform adequately, allowing researchers to draw meaningful inferences.

In contrast, while parametric tests are designed for data that meet specific distributional assumptions, this does not mean they are inflexible or only applicable to perfectly normal data. They can still be effectively used in many practical applications as long as the violations are not extreme. This robustness makes them a preferred choice in many psychological studies, provided the assumptions are reasonably met or the sample size is sufficiently large to mitigate the effects of any violations.