## What is a parametric test?

A parametric test is a test in which the data is assumed to come from a population that is described by a set of parameters. This type of test is used to test hypotheses about the population parameters.

### Why use a parametric test?

Parametric tests are generally more powerful than nonparametric tests, meaning that they have a higher chance of detecting a true difference between two groups. However, parametric tests rely on certain assumptions about the data that may not always be met. When these assumptions are not met, nonparametric tests may be more appropriate.

### How to set up a parametric test

A parametric test is a statistical test that is used to compare two or more populations. The populations can be different sizes, but they must be Normally distributed. The parametric test is based on the assumption that the populations have the same variance.

To set up a parametric test, you will need to:

- Choose a test statistic. This is a numerical value that will be used to compare the populations.
- Calculate the test statistic for each population.
- Compare the test statistics to see if there is a statistically significant difference between the populations.

What is the difference between a parametric and a non-parametric test?

Parametric tests make assumptions about the population being tested while non-parametric tests do not. This means that parametric tests are more powerful and can be used to test for more subtle effects. However, non-parametric tests are more robust and can be used when the assumptions of a parametric test are not met.

When to use a parametric test

A parametric test is used when the data meets the assumptions of normality and equal variances. A parametric test is more powerful than a non-parametric test, which means that it can detect smaller effects. Examples of parametric tests include the t-test, ANOVA, and linear regression.

Non-parametric tests do not make assumptions about the data, which makes them more flexible. However, this also means that they are less powerful and can only detect large effects. Examples of non-parametric tests include the Mann-Whitney U test and the Wilcoxon rank-sum test.

### When to use a non-parametric test

Non-parametric tests make no assumptions about the underlying data distributions, so they are more robust than parametric tests and can be used when data are not Normally distributed. Examples of non-parametric tests include the Wilcoxon rank sum test and the Kruskal-Wallis test.

## How to interpret the results of a parametric test

### What do the results mean?

If the p-value of the runs test is less than 0.05, this suggests that the data is not random, and there is evidence to support the alternative hypothesis. This means that there is a significant difference between the expected number of runs and the actual number of runs in the data.

### How to use the results to improve your website

The results of a parametric test can be used to improve your website in a number of ways. First, you can use the results to identify which areas of your site are performing well and which need improvement. Second, you can use the results to optimize your site for better conversion rates. Finally, you can use the results to improve your overall marketing strategy.