Chi-Square Test
A statistical test used to determine if there is a significant association between categorical variables.
What is Chi-Square Test?
The Chi-Square test of independence compares observed frequencies of categorical data against the frequencies we would expect to see if there was no relationship between the variables in the broader population.
Why Chi-Square Test Matters
It is the primary tool for analyzing cross-tabulated categorical data, such as survey responses where data is grouped into buckets rather than measured continuously.
How to Interpret
A significant p-value indicates that the two categorical variables are dependent (associated).
Example
Testing whether voting preference (Candidate A, Candidate B) is associated with gender (Male, Female, Non-binary). The test compares the observed vote counts in each gender category against what would be expected if gender had no impact on voting.
Common Mistakes
- Using the Chi-Square test when expected cell counts are too small (usually < 5), which invalidates the test approximation (Fisher's Exact Test should be used instead).
- Using it for continuous numerical data instead of categorical data.