Chi-Squared
Chi-squared (χ²) is a statistical test used to determine if there is a significant association between categorical variables or if observed data fits an expected distribution. It calculates the sum of squared differences between observed and expected frequencies, divided by expected frequencies, to produce a test statistic. This test is widely applied in hypothesis testing, such as in goodness-of-fit tests and tests of independence in contingency tables.
Developers should learn chi-squared when working with data analysis, machine learning, or A/B testing to validate assumptions about categorical data, such as checking if user behavior differs across groups or if a model's predictions align with actual outcomes. It's essential for tasks like feature selection in classification problems, analyzing survey results, or ensuring data quality by detecting anomalies in expected distributions.