Parametric and Non-Parametric Tests

Introduction

In Statistics, different tools , methods and tests are used differently with an end purpopse to derive meaningful insights from data . There are several methods used to determine the choice of these tools and methods.Type of data is the major factor in choosing the test conducted. The tests are divided either as parametric or non-parametric tests. Let's differentiate them.

Parametric Tests

Parametric tests are statistical methods that work under the assumption that data follows a specific distribution, most often a normal distribution. They rely on numerical parameters like the mean, standard deviation, and variance to describe and analyze the data. Because they make these assumptions, parametric tests are generally more powerful and accurate when the data fits the expected conditions, allowing researchers to detect even small differences or relationships within their datasets..

Examples:t_test,ANOVA,Pearsons Correlation,Linear Regression.

Non-Parametric Tests

Non-parametric tests are often called distribution-free tests because they do not require the data to follow any particular distribution. These tests are used when data is ordinal, categorical, or not normally distributed. Instead of using numerical values directly, non-parametric tests often rank the data or use medians, making them less affected by extreme values or outliers.Non-parametric tests are especially useful for small sample sizes, non-linear relationships, or when the assumptions of parametric tests cannot be met. Although they are less powerful when compared to parametric tests, they offer flexibility and robustness, ensuring valid conclusions even under irregular data conditions.

Examples:Chi-square test,Spearman's correlation.

Differences

The main difference between parametric and non-parametric tests lies in the assumptions about the data. Parametric tests assume normal distribution, equal variances, and continuous data measured on an interval or ratio scale. Non-parametric tests, however, do not rely on these assumptions and are suitable for ranked or categorical data.

When deciding which test to use, researchers should first check the normality of the data. If the data is normally distributed and sample size is large, a parametric test is appropriate. However, if the data is skewed, ordinal, or has outliers, a non-parametric test will produce more reliable results.

Conclusion

Both parametric and non-parametric tests are essential tools in statistical analysis. Parametric tests are preferred when the data meets their assumptions because they are more efficient and provide stronger conclusions. Non-parametric tests, on the other hand, offer flexibility and reliability when data does not meet the strict assumptions of normality or when dealing with ranked or categorical data.