Parametric and Non-Parametric Tests

In the realm of medical statistics and research, understanding the distinction between parametric and non-parametric tests is crucial for appropriate data analysis. These tests are fundamental tools to infer conclusions from data, test hypotheses, and drive scientific discoveries in medicine.

Parametric Tests:

Parametric tests are based on assumptions regarding the distribution of the underlying population from which the sample is drawn. These tests are typically applied when the data are assumed to be normally distributed and when the scale of measurement is interval or ratio. They are powerful and provide precise results if their assumptions are met.

Non-Parametric Tests:

Non-parametric tests, on the other hand, do not rely on assumptions about the population's distribution. These tests are used when the data do not meet the assumptions necessary for parametric testing, such as when dealing with ordinal data or skewed distributions. They are more versatile but generally less powerful than parametric tests.