Math & Science
Chi-Square Calculator
Perform chi-square tests for independence and goodness of fit
Formulas and edge cases are reviewed against authoritative references before publication. For methodology, editorial standards, or corrections, use the links below.
Frequently asked questions
When to use chi-square?
For categorical data - testing if variables are independent or if data fits expected distribution.
What are the assumptions?
Expected frequencies should be at least 5 in each cell. Sample should be random.
How to interpret p-value?
If p < 0.05, reject null hypothesis - variables are likely related/distribution doesn't fit.
Related tools
About this tool
Inputs
- Observed Values
- Expected Values (optional)
- Test Type
- Independence
- Goodness of fit
Results
- Chi-Square Statistic
- Degrees of Freedom
- P-Value
- Conclusion
- Statistically significant
- Not significant
Before making a decision based on estimates, run the numbers through this Chi-Square Calculator. A few seconds of input can save hours of uncertainty. Start by filling in observed values, expected values (optional), test type, independence and goodness of fit. Based on these values, the tool computes chi-square statistic, degrees of freedom, p-value and other key metrics. Mathematical precision depends on the number of significant figures — more digits mean less rounding error.
Many practical problems — from engineering to daily budgets — reduce to straightforward arithmetic once you identify the right formula. Double-check units before and after the calculation — a mismatched unit is the most common source of wrong answers. Having a dedicated tool to perform chi-square tests for independence and goodness of fit saves time you would otherwise spend searching for formulas or setting up a spreadsheet. Precision matters: rounding at intermediate steps can introduce errors that compound in multi-step calculations. If the result surprises you, revisit your inputs — a mistyped digit or wrong unit is usually the culprit. Rounding to the appropriate number of significant figures avoids false precision.