# CHISQ.DIST

The CHISQ.DIST function calculates the probability density function or the cumulative distribution function of a chi-squared 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.

## Usage

Use the CHISQ.DIST formula with the syntax shown below, it has 3 required parameters:

Parameters:
1. x (required):
The value at which you want to evaluate the distribution.
2. degrees_freedom (required):
The number of degrees of freedom. Must be a positive integer.
3. cumulative (required):
A logical value that determines the form of the function. Use TRUE for the cumulative distribution function and FALSE for the probability density function.

## Examples

Here are a few example use cases that explain how to use the CHISQ.DIST formula in Google Sheets.

### Testing for independence

Suppose you have two categorical variables and you want to determine if they are independent. You can use the CHISQ.DIST function to calculate the p-value of the chi-squared test.

### Goodness of fit test

Suppose you have a sample of data and you want to determine if it follows a specific distribution. You can use the CHISQ.DIST function to calculate the p-value of the goodness of fit test.

### Estimating confidence intervals

You can use the CHISQ.DIST function to calculate the critical value of a chi-squared distribution. This can be used to estimate confidence intervals for a population variance.

## Common Mistakes

CHISQ.DIST not working? Here are some common mistakes people make when using the CHISQ.DIST Google Sheets Formula:

### Incorrect input order

Users often input the arguments in the wrong order, leading to incorrect results. Make sure to input x first, followed by degrees_freedom and then cumulative.

### Incorrect degrees of freedom

Users sometimes input the wrong degrees of freedom, which can drastically affect the results. Double-check your degrees of freedom to ensure they are correct.

### Forgetting to set cumulative

The CHISQ.DIST formula requires the input of the 'cumulative' argument. If this argument is not set, it will default to 1, which may not be what you want. Make sure to set cumulative to either 0 or 1, depending on your needs.

The following functions are similar to CHISQ.DIST or are often used with it in a formula:

• CHISQ.INV

The CHISQ.INV function returns the inverse of the right-tailed probability of the chi-squared distribution. It is commonly used in hypothesis testing where the null hypothesis is that the observed data follows a chi-squared distribution. The function returns the value of the chi-squared random variable at which the cumulative distribution function equals the given probability. This function is useful for finding critical values after performing a chi-squared test.

• CHISQ.TEST

The CHISQ.TEST formula calculates the test for independence of two categorical ranges of data using the chi-squared 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.

• T.DIST

The T.DIST function returns the probability of a Student's t-distribution with a specified degrees of freedom. This function is usually used in hypothesis testing to determine the probability that a sample mean is within a specified range of values.

• T.TEST

The T.TEST function calculates the probability associated with a Student's t-test. 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.

• NORM.DIST

The NORM.DIST formula is a statistical function that returns the normal distribution of a specified variable. It is used to determine the probability of a random variable falling within a specified range of values. This function is commonly used in finance and scientific research.