Chi-Square Test for Independence Calculator
Determine the association between two categorical variables with our easy-to-use calculator.
Input Data
Enter your categorical data for Variable 1 and Variable 2, separated by commas. Ensure both inputs have the same number of entries.
Results
Contingency Table (Observed Frequencies)
| Variable 2 | Row Total | |
|---|---|---|
| Column Total |
Expected Frequencies
| Variable 2 | |
|---|---|
Understanding Chi-Square Test
The Chi-Square test of independence is a statistical tool used to examine if there's a significant association between two categorical variables. It compares observed frequencies in a contingency table with expected frequencies under the assumption of no association.
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis (independence), suggesting a significant association between the variables. Conversely, a large p-value suggests no significant association.
This test is widely applied in fields like social sciences, market research, and healthcare to analyze relationships between categorical data such as survey responses, treatment outcomes, or demographic groups.