CHISQ.TEST
TheCHISQ.TEST
formula calculates the test for independence of two categorical ranges of data using the chisquared distribution. It returns the probability that any observed differences between the two ranges are due to chance. This formula is commonly used in hypothesis testing to determine whether there is a significant association between two variables.
 How to use
CHISQ.TEST
formula?  Examples of using
CHISQ.TEST
formula CHISQ.TEST
formula not working? Similar formulas to
CHISQ.TEST
Usage
Use the CHISQ.TEST
formula with the syntax shown below, it has 2 required parameters:
 observed_range (required):
The range of observed categorical data.  expected_range (required):
The range of expected categorical data, with the same dimensions as the observed range.
Examples
Here are a few example use cases that explain how to use theCHISQ.TEST
formula in Google Sheets.
Testing for association between two variables
Suppose you have two categorical variables and you want to test whether there is a significant association between them. You can use the CHISQ.TEST
formula to calculate the probability that any observed differences between the two variables are due to chance.
Testing goodness of fit
Suppose you have a categorical variable and you want to test whether the observed frequencies of the categories fit a specified distribution. You can use the CHISQ.TEST
formula to calculate the probability that any differences between the observed and expected frequencies are due to chance.
Testing homogeneity of proportions
Suppose you have two or more populations and you want to test whether they have the same proportions of a categorical variable. You can use the CHISQ.TEST
formula to calculate the probability that any observed differences between the populations are due to chance.
Common Mistakes
CHISQ.TEST
not working? Here are some common mistakes people make when using the CHISQ.TEST
Google Sheets Formula:
Incorrect range size
One common mistake is to provide observed_range and expected_range that have different sizes. Make sure both ranges have the same number of cells.
Incorrect range reference
Another common mistake is to provide incorrect range references for observed_range and expected_range. Doublecheck the cell references to ensure they are correct.
Invalid input data
The CHISQ.TEST formula requires nonnegative input values. If any of the values in the observed_range or expected_range are negative, the formula will return an error. Check your data to ensure it meets this requirement.
Not enough data
The CHISQ.TEST formula requires at least one degree of freedom. If the number of cells in the observed_range or expected_range is less than 2, the formula will return an error. Make sure you have enough data to perform the test.
Incorrectly interpreting results
One common mistake is to misinterpret the results of the CHISQ.TEST formula. Remember that the formula returns a pvalue, which is the probability of getting the observed result by chance alone. A small pvalue (less than 0.05) suggests that the observed and expected data are significantly different, while a large pvalue (greater than 0.05) suggests that they are not significantly different.
Related Formulas
The following functions are similar to CHISQ.TEST
or are often used with it in a formula:

CHISQ.DIST
The
CHISQ.DIST
function calculates the probability density function or the cumulative distribution function of a chisquared distribution. This function is commonly used in hypothesis testing to determine the significance of the difference between expected and observed values. The output of this function can be used to make decisions about the null hypothesis. 
CHISQ.INV
The CHISQ.INV function returns the inverse of the righttailed probability of the chisquared distribution. It is commonly used in hypothesis testing where the null hypothesis is that the observed data follows a chisquared distribution. The function returns the value of the chisquared random variable at which the cumulative distribution function equals the given probability. This function is useful for finding critical values after performing a chisquared test.

F.TEST
The
F.TEST
function returns the result of an Ftest, which tests the equality of variances between two datasets. It is commonly used to compare the variances of two datasets to determine if they are significantly different. If the result is less than or equal to a critical value, the variances are considered equal. If the result is greater than the critical value, the variances are considered unequal. 
T.TEST
The
T.TEST
function calculates the probability associated with a Student's ttest. This function is commonly used in statistics to determine whether two samples are likely to have come from the same two underlying populations that have the same mean. It returns the probability that the two samples are different. The function assumes that the two samples are independent of each other and have equal variances. 
Z.TEST
The
Z.TEST
formula returns the probability of a certain value occurring in a normal distribution. This formula is commonly used to test whether two samples have the same mean by comparing their Z scores. The formula returns a value between 0 and 1, with a result closer to 1 indicating a higher likelihood that the value could occur by chance.
Learn More
You can learn more about the CHISQ.TEST
Google Sheets function on Google Support.