Survey Statistics
Chi-Square Test Calculator
Test whether there's a statistically significant relationship between two categorical variables in your survey data.
Enter Your Contingency Table
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| Category A | Category B | |
|---|---|---|
| Group 1 | ||
| Group 2 |
What is a Chi-Square Test?
The Chi-square test determines if there's a significant association between categorical variables. For example: Is there a relationship between gender and product preference in your survey?
Common Survey Use Cases
- • Age group vs. brand preference
- • Gender vs. purchase decision
- • Region vs. satisfaction level
- • Education vs. response choice
Interpreting p-values
- ✓ p < 0.05: Significant relationship
- ~ p = 0.05–0.10: Marginal significance
- ✗ p > 0.10: No significant relationship
Helpful Guides
Understanding Chi-Square Tests
When to Use This Test
Use the Chi-square test when you have categorical data (like survey response options) and want to see if the distribution of responses differs between groups. Both variables must be categorical, not numerical.
Requirements
- • Both variables must be categorical
- • Expected frequency in each cell should be ≥5
- • Observations must be independent