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Chi-Square Test for Independence
Formula
The chi-square test compares observed frequencies with expected frequencies to determine whether there is a statistically significant difference. It is widely used in hypothesis testing for categorical data.
A larger chi-square value indicates a greater discrepancy between observed and expected values, suggesting the variables may not be independent.
Common use cases:
- Testing independence between categorical variables
- Goodness-of-fit testing
- A/B testing in marketing
Frequently Asked Questions
Dr. David Park
Applied Mathematician, PhD Mathematics
David holds a PhD in Applied Mathematics from MIT. He has published research on numerical methods and computational algorithms used in engineering and scientific calculators.
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