- Discuss the assumptions of parametric statistical testing versus the assumptions of nonparametric tests. Discuss why a researcher would select a nonparametric approach based on the data and when they would select parametric tests for their data set. Does it matter what type of variables have been collected in the dataset?

**Expert Solution Preview**

Introduction: As a medical professor responsible for teaching and evaluating medical college students, it is important to understand the assumptions of parametric and nonparametric statistical testing and when to choose one over the other.

Parametric statistical testing assumes that the data being analyzed comes from a normal distribution with equal variances. This means that the data is continuous, the sample size is large enough, and the data is independent. On the other hand, nonparametric testing does not assume any underlying distribution of the data and is used when the data does not meet the assumptions of parametric tests. Nonparametric tests are also used when the data is ordinal or nominal.

A researcher would select a nonparametric approach when the assumptions of parametric tests are not met. For example, if the data is skewed or has outliers, a nonparametric test would be more appropriate. Nonparametric tests are also used when the sample size is small or when the data is not normally distributed.

Parametric tests are selected when the data meets the assumptions of normality and equal variances. They are more powerful than nonparametric tests and can detect smaller differences between groups. Generally, parametric tests are used when the data is continuous and normally distributed. However, the type of variables collected in the dataset does matter. If the data is not continuous or normally distributed, nonparametric tests would be more appropriate.

In conclusion, understanding the assumptions of parametric and nonparametric statistical testing and when to choose one over the other is important for medical college students. Nonparametric tests are used when the assumptions of parametric tests are not met or when the data is ordinal or nominal. Parametric tests are selected when the data meets the assumptions of normality and equal variances. However, the type of variables collected in the dataset does matter.