Chi-Square Calculator
Calculate chi-square statistic and p-value from a 2×2 contingency table.
Chi-Square
16.6667
p-value
0.0000
Results
| Chi-Square Statistic | 16.6667 |
| p-value (approx) | 0.0000 |
| Degrees of Freedom | 1 |
| Significance (α=0.05) | Highly Significant |
| Expected [1,1] | 30.00 |
| Expected [1,2] | 20.00 |
| Expected [2,1] | 30.00 |
| Expected [2,2] | 20.00 |
Use the Chi-Square Calculator above to calculate your results. Enter your values and see instant results — all calculations run in your browser.
Disclaimer: This calculator is for informational purposes only and does not constitute tax, financial, or legal advice. Results are estimates based on the information you provide and current rates. Always consult a qualified tax professional or financial advisor for advice specific to your situation.
How It Works
This Chi-Square Calculator determines the statistical significance of the association between two categorical variables, specifically from a 2x2 contingency table. In 2026, understanding such associations is crucial for fields like market research, where a company might analyze if a new ad campaign (variable 1) significantly impacts customer purchase intent (variable 2), or in healthcare, assessing if a new treatment is significantly more effective than a placebo for a specific patient outcome.
The calculator computes the Chi-Square (χ²) statistic using the formula: Σ((Observed - Expected)² / Expected). Expected frequencies are calculated as (Row Total × Column Total) / Grand Total for each cell. This statistic is then used to determine the p-value, which represents the probability of observing such an association by chance, assuming no true relationship between the variables.
A common mistake is misinterpreting a small p-value as a large effect size; it only indicates statistical significance, not practical importance. Ensure your expected cell counts are not too small (generally, no more than 20% of cells should have expected counts less than 5) as this can invalidate the Chi-Square test's assumptions, leading to inaccurate p-values.
Example: Analyzing Customer Preference for a New Product Feature
- 1 A tech company, 'InnovateCorp,' surveyed 500 potential customers in Q1 2026 to see if their gender influences preference for a new AI-powered product feature. The results were: 150 men preferred the feature, 100 men did not; 120 women preferred the feature, 130 women did not. Input these values into the 2x2 table.
- 2 Input: Preferred (Men: 150, Women: 120), Not Preferred (Men: 100, Women: 130). The calculator will compute expected values. For instance, Expected (Men, Preferred) = (250 * 270) / 500 = 135.
- 3 The calculated Chi-Square (χ²) statistic is 8.01. This value reflects the discrepancy between the observed and expected frequencies under the assumption of no association.
- 4 The p-value is 0.0046. Since 0.0046 is less than the common significance level of 0.05, InnovateCorp can conclude with 95% confidence that there is a statistically significant association between gender and preference for the new AI-powered product feature among their potential customers in Q1 2026.
Source: Khan Academy · Last updated: April 2026
Frequently Asked Questions
When do you use a chi-square test?
How do you interpret a chi-square p-value?
What is the chi-square formula?
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