A **Q-Q plot**, short for “quantile-quantile” plot, is often used to assess whether or not a variable is normally distributed.

This tutorial explains how to create and interpret a Q-Q plot in SPSS.

**Example: Q-Q Plot in SPSS**

Suppose we have the following dataset in SPSS that displays the points per game for 25 different basketball players:

We can use the following steps in SPSS to create a Q-Q plot to determine whether or not the variable **points **is normally distributed.

**Step 1: Choose the Explore option.**

Click the **Analyze **tab, then **Descriptive Statistics**, then **Explore**:

**Step 2: Create the Q-Q plot.**

Drag the variable **points **into the box labelled Dependent List. Then click the button labelled **Plots **and make sure the box is checked next to **Normality plots with tests**. Then click **Continue**. Then click **OK**.

**Step 3: Interpret the Q-Q plot.**

Once you click **OK**, the following Q-Q plot will be displayed:

The idea behind a Q-Q plot is simple: if the residuals fall along a roughly straight line at a 45-degree angle, then the residuals are roughly normally distributed.

We can see in our Q-Q plot above that the residuals tend to deviate from the 45-degree line quite a bit, especially on the tail ends, which could be an indication that they’re not normally distributed.

Although a Q-Q plot isn’t a formal statistical test, it offers an easy way to visually check whether or not the residuals are normally distributed.

For two formal statistical tests, refer to the p-values from the Kolmogorov-Smirnov Test and the Shapiro-Wilk Test displayed above the Q-Q plot:

- P-value of Kolmogorov-Smirnov Normality Test:
**.086** - P-value of Shapiro-Wilk Normality Test:
**.042**

Since both of these values are close to .05, this is an indication that the variable **points** may not be normally distributed.