Question

. Nonparametric statistics might be preferred under which of the following conditions?

1. Your sample size is very small.
2. Your sample size is very large.
3. The variables you are analyzing are continuous.
4. All the assumptions of parametric statistics have been met.

Answer 1

Nonparametric tests are also called distribution-free tests because they don't assume that your data follow a specific distribution. You may have heard that you should use nonparametric tests when your data don't meet the assumptions of the parametric test, especially the assumption about normally distributed data.

1. Your sample size is very small.

Answer 2

Nonparametric statistics might be preferred under the following conditions:

1. Your sample size is very small: Nonparametric tests are often more robust and less sensitive to violations of assumptions when the sample size is small. Parametric tests may rely on assumptions about the distribution of the data, which may not hold with small sample sizes.

2. The variables you are analyzing are continuous: Nonparametric tests can be used for both continuous and categorical variables. However, they are particularly useful when dealing with continuous variables that do not follow a specific distribution or when the assumptions of parametric tests are not met.

3. All the assumptions of parametric statistics have not been met: Parametric tests often have assumptions about the underlying distribution of the data, such as normality or homogeneity of variance. If these assumptions are not met, nonparametric tests can provide valid statistical inference without relying on these assumptions.

Therefore, nonparametric statistics may be preferred when the sample size is small, the variables are continuous, or the assumptions of parametric statistics have not been met.