Level 1 CFA® Exam:
Parametric Tests vs Nonparametric Tests

Division of tests into parametric and nonparametric is said to be one of the most important in hypothesis testing.

Parametric tests, which we talked about in our previous videos, are statistical tests that are used in hypothesis testing that involves population parameters such as the mean or the variance.

Nonparametric tests are used mainly in the following situations:

• when the hypothesis is not related to any parameter,
• when we make no or minimal assumptions about the population distribution, or
• when data are given in ranks.

Parametric tests are generally better and more accurate than nonparametric ones. They are characterized by a broader range of assumptions that need to be satisfied. Also they are more powerful and their results are easy to interpret.

One of the examples of nonparametric test is a test based on the Spearman rank correlation coefficient.

It is one of the most frequently used tests to examine the correlation between two variables. We often employ it when we cannot use t-test because random variables don’t meet assumptions about the distribution. We discuss this test in the next lesson.

1. Parametric tests are statistical tests that are used in hypothesis testing that involves population parameters such as the mean or the variance.
2. Nonparametric tests are used wherever the collected data do not meet the strict assumptions of parametric tests or data are given in ranks or the hypothesis is not about the parameter.
3. Nonparametric tests are less accurate and more difficult to interpret.