In statistics, there are two common types of Chi-Square tests in which you will have to calculate expected counts:

**1.** The Chi-Square Goodness of Fit Test – Used to determine whether or not a categorical variable follows a hypothesized distribution.

**2.** The Chi-Square Test of Independence – Used to determine whether or not there is a significant association between two categorical variables.

The following examples show how to calculate expected counts for each of these tests.

**Example 1: Expected Counts for Chi-Square Goodness of Fit Test**

Suppose a store owner claims that an equal number of customers come into his shop each weekday.

To test this hypothesis, he records the number of customers that come into the shop on a given week and finds the following:

To find the **expected count** of customers each day, we can use the following formula:

**Expected count = Expected percentage * Total count**

Recall that the shop owner expects an equal amount of customers to come into the shop each day. Thus, the expected percentage of customers that come in on a given day is 20% of the total customers for the week.

This means we can calculate the expected frequency of customers each day as:

Expected count = 20% * 250 total customers = **50**

Once we have the expected counts, we can proceed to calculate the Chi-Square test statistic and the corresponding p-value to determine if the shop owner’s claim is likely to be true.

**Note**: This tutorial explains how to perform this exact Chi-Square Goodness of Fit test in Excel.

**Example 2: Expected Counts for Chi-Square Test of Independence**

Suppose we want to know whether or not gender is associated with political party preference.

We take a simple random sample of 500 voters and survey them on their political party preference. The following table shows the results of the survey:

To calculate the **expected count** of each cell in the table, we can use the following formula:

**Expected count = (row sum * column sum) / table sum**

For example, the expected value for Male Republicans is: (230*250) / 500 = **115**.

We can repeat this formula to obtain the expected value for each cell in the table:

Once we have the expected counts, we can proceed to calculate the Chi-Square test statistic and the corresponding p-value to determine if there is a statistically significant association between gender and political party preference.

**Note**: This tutorial explains how to perform this exact Chi-Square Test of Independence in Excel.

**Additional Resources**

The following resources provide additional information about Chi-Square tests:

Chi-Square Goodness of Fit Test Calculator

Chi-Square Test of Independence Calculator

When to Use a Chi-Square Test (With Examples)