In statistics, a **sample proportion (p̂)** represents the proportion of observations in a sample with a certain characteristic.

This is calculated as:

**p̂ = x / n**

where:

**x:**The count of individuals in the sample with a certain characteristic.**n:**The total number of individuals in the sample.

To quantify the uncertainty around our estimate for the sample proportion, we can calculate the **standard error of proportion**, which is calculated as:

**Standard Error of Proportion = √p̂(1-p̂) / n**

where:

**p̂ :**The sample proportion.**n:**The total number of individuals in the sample.

The following example shows how to calculate a standard error of proportion in Excel.

**Example: Calculate Standard Error of Proportion in Excel**

Suppose we would like to estimate the proportion of students in a certain school who support a new homework rule.

To do so, we randomly survey 20 students and mark **1** if they support the new rule or **0** if they don’t:

We can type the following formulas into cells **E1** and **E2** to calculate the sample proportion and the standard error of the proportion, respectively:

**E1**: =COUNTIF(B2:B21, 1)/COUNTA(B2:B21)**E2**: =SQRT(E1*(1-E1)/COUNTA(B2:B21))

The following screenshot shows how to use these formulas in practice:

From the output we can see the following:

- Sample Proportion:
**0.45** - Standard Error of Proportion:
**0.111243**

This tells us that **0.45** (or 45%) of students support the new homework rule and the standard error of this estimate is **0.111243**.

**Additional Resources**

The following tutorials explain how to perform other common operations in Excel:

How to Calculate the Standard Error of the Mean in Excel

How to Calculate the Standard Error of Regression in Excel

How to Add Custom Error Bars in Excel