Categorical Data Analysis: The Chi-Square Test in Statistics Explained Simply

Welcome to this deep dive into the Chi-Square Test ($\chi^2$), the essential tool for analyzing categorical data! If you work with counts, labels, or nominal variables, this video is your masterclass in statistical analysis. We will cover the two main types of the chi square test: Goodness of Fit Test: Learn how to compare your observed sample counts against a known theoretical distribution. (e.g., checking if candy colors match the company's claim). Test of Independence: Discover how to determine if two categorical variables are related, or if they are statistically independent (e.g., Is "Gender" independent of "Voting Preference"?). We break down the mathematical heart of the test: the chi square formula ($\chi^2 = \sum \frac{(O_i - E_i)^2}{E_i}$). You will master calculating the Degrees of Freedom for both tests and understand how the value determines the shape of the Chi-Square Distribution. Finally, learn how to use the $\chi^2$ statistic to find your p-value and make confident decisions to reject the null hypothesis. This is crucial for A/B testing, clinical trials, and general data analysis. Don't forget to check the critical assumptions, like having a minimum expected frequency of 5 in each cell (Cochran's Rule). Like, subscribe, and let's conquer categorical data analysis together!