# Statistics: Pearson’s chi-squared test

This is part of the course “Probability Theory and Statistics for Programmers”.

**Pearson’s chi-squared test** is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. **Categorical data** is statistical data consisting of categorical variables(a variable that can take on one of the limited possible values). Example of categorical data will be subway usage on specific days:

In simple words, by using Pearson’s chi-squared test we can accept or reject the null hypothesis. Usually, a null hypothesis represented in the way of an **expected **set of data and an alternative hypothesis as some newly **observed **data.

Let’s take a look at the example. We have data about students' dropout/graduation rates(null hypothesis).

Recently we decided to make research on this topic and as a result, received this data:

Assuming the old data was correct what how would expected data look for given sample size?

With this table in place we can calculate Chi-Square statistic using this formula:

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Now, when we have Chi-square statistic it is the right time to accept or reject the null hypothesis. But first, we need to choose significance level. The most common choice for significance level is *0.05*, so we will use it in our example.

As you might guess we will use Chi-squared distribution function. The degree of freedom for distribution in Pearson test equal to *n-1*, where *n *is the number of categories. In our example, there are *5 *categories, therefore the degree of freedom is equal to *4*.

As you can see our value is much larger than the critical value, therefore, we will reject the null hypothesis.

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