Chi-squared Tests
Chi-squared tests are statistical hypothesis tests that are used to determine whether there is a significant association between two categorical variables. The test compares the observed data with the expected data, assuming that there is no significant difference between the two. The test calculates a chi-squared statistic, which is then compared to a critical value from a chi-squared distribution to determine whether the null hypothesis should be rejected or not. Chi-squared tests are commonly used in data science and artificial intelligence to analyze categorical data, such as survey responses or website click-through rates, and to determine whether there is a significant relationship between different variables. There are several types of chi-squared tests, including the Pearson chi-squared test, the likelihood ratio chi-squared test, and the Fisher's exact test.
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