Parametric vs Non-Parametric Statistical Tests

1. 1Identify the type of data you are working with to determine if a parametric or non-parametric test is suitable
2. 2Choose a parametric test when your data follows a normal distribution and meets other assumptions
3. 3Select a non-parametric test when your data doesn't meet the assumptions of parametric tests, such as non-normal distribution or ordinal data
4. 4Compare the results of parametric and non-parametric tests to see which one is more appropriate for your data
5. 5Apply the chosen test to your data using statistical software or programming languages like R or Python

Data scientists and analysts benefit from understanding the distinction between parametric and non-parametric tests to choose the right approach for their data analysis

💡 Parametric tests assume normal distribution and equal variances, while non-parametric tests don't require these assumptions

💡 Parametric vs Non-Parametric Statistical Tests: Know the difference to choose the right test for your data!