Perform chi-square test of independence for categorical data analysis.
Categorical data analysis
Chi-square test is used to test whether there is a significant relationship between two categorical variables. For example, is there a relationship between gender and product preference? Are education level and income independent? How it works: Observed frequencies are compared with expected frequencies. If there's a large difference, variables are not independent. Degrees of freedom (df) = (rows-1) × (columns-1). Each cell should have at least 5 observations. Applications and examples: Marketing research (age group → product preference), health research (treatment → recovery rate), social sciences (education → income relationship), quality control (shift → error rate), A/B testing (version → conversion rate).
Find answers to common questions
Use it to test relationship between two categorical variables. Examples: gender-product preference, education-income, age group-disease.
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Enter data row by row, separated by comma or space
Conclusion
Significant relationship exists between variables (reject H₀)
| 12.00 | 18.00 |
| 28.00 | 42.00 |
Note: Chi-square test tests independence between categorical variables. If χ² > critical value or p < α, relationship exists between variables.