CHISQ.TEST function

The CHISQ.TEST function in Excel is used to perform the chi-squared test for independence or goodness of fit. It compares the observed values in a contingency table or dataset with the expected values, and it returns the p-value for the test. The p-value helps you assess whether the differences between observed and expected values are statistically significant.

Syntax:

CHISQ.TEST(actual_range, expected_range)

Arguments:

• actual_range: The range of observed values (the actual data you collected).
• expected_range: The range of expected values (the values you would expect if there were no significant difference).

How it works:

The chi-squared test evaluates how closely the observed data match the expected data. It is typically used for:

• Goodness of fit: To test if a sample matches an expected distribution.
• Test of independence: To test if two categorical variables are independent.

Example:

If you have a 2×2 table where A1:B2 contains your observed frequencies, and the expected frequencies are in the range C1:D2, you would use:

=CHISQ.TEST(A1:B2, C1:D2)

This will return the p-value for the chi-squared test, which you can compare with a significance level (such as 0.05) to determine whether the observed data significantly differ from the expected data.

Interpreting the result:

• A small p-value (typically less than 0.05) indicates that there is a significant difference between the observed and expected values, suggesting that the variables are not independent or that the data does not fit the expected distribution.
• A large p-value suggests that there is no significant difference, meaning that any observed differences are likely due to random chance.