1. Parametric Tests
Parametric tests are statistical tests that assume the data follows a known and specific distribution, usually a normal distribution.
Thank you for reading this post, don't forget to subscribe!- They require numerical data measured on an interval or ratio scale.
- These tests use parameters such as the mean and standard deviation.
Examples include the t-test, z-test, ANOVA, and Pearson’s correlation. Parametric tests are generally more powerful when their assumptions are met.
2. Non-Parametric Tests
Non-parametric tests are statistical tests that do not assume any specific distribution of the data.
- They are useful when data is ordinal, categorical, or not normally distributed.
- These tests are more flexible and can be used for small sample sizes or skewed data.
Examples include the Chi-square test, Mann–Whitney U test, Kruskal–Wallis test, and Spearman’s rank correlation.
Important Non-Parametric Tests for Hypothesis Testing
- Chi-Square Test
- Correlation
- Regression Analysis
- Time Series Analysis
- Multivariate Analysis
Difference Between Parametric and Non-Parametric Tests
| Parametric Tests | Non-Parametric Tests |
|---|---|
| Parametric tests assume that the data follows a normal or known distribution. | Non-parametric tests do not assume any specific distribution of the data. |
| Parametric tests require quantitative data measured on an interval or ratio scale. | Non-parametric tests can be used with ordinal, nominal, or non-normally distributed data. |
| Parametric tests use population parameters like the mean and standard deviation for analysis. | Non-parametric tests rely on ranks, frequencies, or signs instead of population parameters. |
| Parametric tests are more powerful when their assumptions are satisfied. | Non-parametric tests are less powerful but more flexible when assumptions are not met. |
| Larger sample sizes are generally required for parametric tests to be valid. | Non-parametric tests can be used effectively with small sample sizes. |
| Examples include t-test, z-test, ANOVA, and Pearson correlation. | Examples include Chi-square test, Mann–Whitney U test, Kruskal–Wallis test, and Spearman correlation. |
| Parametric tests provide more precise and reliable estimates when conditions are met. | Non-parametric tests provide safer results when the data violates parametric assumptions. |