Let me take you into the universe of chi-square tests and how we can involve them in Python with the scipy library. We'll be going over the chi-square integrity of the fit test. Whether the reader is just starting or an accomplished information examiner, this guide will outfit you with pragmatic models and experiences so you can unhesitatingly apply chi-square tests in your own work. This article was published as a part of the Data Science Blogathon. The Chi-Square test is one of the fact-based interactions used to assess the connection between two all-out factors to figure out the connection between them.

In one of our previous articles, we discussed how to calculate the test statistic in a chi-square test of independence or goodness-of-fit test. We also discussed that the test statistic in a chi-square test follows the chi-square distribution. So, how can we calculate the p-value from the test statistic in a chi-square test? In this article, we will discuss that. Let's say our test statistic is 6.4, and the degrees of freedom is 5. Here, we are using the chi2.sf()

The chi-square statistic is a useful tool for understanding the relationship between two categorical variables. For the sake of example, let's say you work for a tech company that has rolled out a new product and you want to assess the relationship between this product and customer churn. In the age of data, tech or otherwise, many companies undergo to risk of taking evidence that is either anecdotal or perhaps a high level visualization to indicate certainty of a given relationship. The chi-square statistic gives us a way to quantify and assess the strength of a given pair of categorical variables. Let's explore chi-square from this lens of customer churn.