Which test is typically used for data sets larger than fifty subjects?

The Kolmogorov-Smirnov test is particularly suitable for larger data sets, typically those with more than fifty subjects, because it is a non-parametric method that evaluates the distribution of a variable and compares it to a reference probability distribution. This test is powerful in assessing whether a sample comes from a specific distribution, such as a normal or uniform distribution, especially when the sample size is sufficiently large. With larger samples, the test gains statistical power and can detect even minor deviations from the expected distribution, providing valuable insights for researchers in clinical psychology about the underlying characteristics of their data.

In addition to its applicability for larger sample sizes, the Kolmogorov-Smirnov test doesn’t rely on the normality assumption of the sample data, which can be a limitation of other tests used for smaller samples. This versatility is key when handling diverse types of data commonly encountered in clinical research. Therefore, its robust nature in analyzing larger datasets makes it a preferred choice in such scenarios.