ChiSquare

Photo by Kelly Sikkema on UnsplashChi-square is a non-parametric test, i.e., it does not require normal distribution or variance assumptions about the populations from which the samples are drawn.The general purpose of the Chi-square test is to compare discrete categorical data (count data). For example, a product may be categorized into two categories, such as defective/non-defective, or in more than two categories, such as excellent, good, fair, and poor. Chi-square tests are ideally suited to a data set in which both…

Complete Guideline to Find Dependencies among Categorical Variables with Chi-Square TestPhoto by Kier In Sight on UnsplashMotivationMany statistical tests are available, like the p-test, z-test, students’ t-test, ANOVA test, etc. Why do we need the Chi-Square test? None of the above tests show the dependency of categorical variables. Chi-Square test is the best statistical weapon for doing the job. The main idea behind the test is the following clause —“Goodness of fit.”Let’s make it a little bit easier with an example.…