The Difference Between Parametric and Non-Parametric Tests Explained
When it comes to conducting statistical analyses, researchers have two types of tests at their disposal: parametric and non-parametric tests. While both types of tests are designed to extract meaningful insights from data, they differ in terms of their assumptions and the types of data they can analyze.
Basics of Parametric Tests
Parametric tests are statistical tests that examine the relationship between one or more variables. These tests require that the data being analyzed conform to certain assumptions about the population distribution, making them best suited for analyzing data that is normally distributed.
Parametric tests rely on specific calculations that take into account measures like means, variances, and standard deviations. Some examples of parametric tests include:
- t-tests
- ANOVA
- Regression analyses
Basics of Non-Parametric Tests
Non-parametric tests, on the other hand, are statistical tests that do not rely on assumptions about the population distribution. These tests are better suited for analyzing data that is not normally distributed or where the data may have outliers or other anomalies that violate the assumptions underlying parametric tests.
Non-parametric tests do not rely on specific calculations that use means and standard deviations. Instead, they use methods like ranking or permutation tests to analyze the data. Examples of non-parametric tests include:
- Mann-Whitney U-test
- Wilcoxon Signed Rank test
- Chi-square test
Which Test to Use?
Deciding between a parametric and non-parametric test largely depends on the nature of the data being analyzed. If the data follows a normal distribution and meets the assumptions of the chosen parametric test, a researcher would typically use parametric testing.
However, if the data is not normally distributed or violates the assumptions of a normal distribution, a non-parametric test would be more appropriate.
In general, non-parametric tests tend to be more robust and versatile in handling various types of data, but they may lack the same level of statistical power and precision as parametric tests.
In conclusion, understanding the difference between parametric and non-parametric tests is essential for researchers looking to conduct insightful statistical analyses. By selecting the appropriate test for the data at hand, researchers can ensure they are drawing accurate and valuable conclusions from their research.
Table difference between parametric and non parametric tests
| Parameter | Parametric Tests | Non-Parametric Tests |
|---|---|---|
| Definition | Statistical tests that assume the data follows a normal distribution and can be described using parameters such as mean and variance. | Statistical tests that do not assume any specific distribution and do not rely on parameters. |
| Types of Data | Used for interval or ratio data | Used for nominal or ordinal data |
| Assumptions | Assumes normality, equal variances and independence | No assumptions regarding distribution or variance |
| Examples | t-test, ANOVA, Pearson’s correlation | Wilcoxon rank-sum test, Kruskal-Wallis test, Spearman’s correlation |
| Significance | More powerful and precise in detecting significant differences | Less powerful and precise in detecting significant differences |