# Interpret the key results for Outlier Test

Complete the following steps to interpret an outlier test. Key output includes the p-value, the outlier, and the outlier plot.

## Step 1: Determine whether an outlier exists

To determine whether an outlier exists, compare the p-value to the significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well. A significance level of 0.05 indicates a 5% risk of concluding that an outlier exists when no actual outlier exists.
P-value ≤ α: An outlier exists (Reject H0)
If the p-value is less than or equal to the significance level, the decision is to reject the null hypothesis and conclude that an outlier exists. Try to identify the cause of any outliers. Correct any data–entry errors or measurement errors. Consider removing data values that are associated with abnormal, one-time events (special causes).
P-value > α: You cannot conclude an outlier exists (Fail to reject H0)
If the p-value is greater than the significance level, the decision is to fail to reject the null hypothesis because you do not have enough evidence to conclude that an outlier exists. You should make sure that your test has enough power to detect an outlier. For more information, go to Increase power.

## Step 2: Determine the value of the outlier

If the test identifies an outlier in the data, then Minitab displays an outlier table. Use the outlier table to determine the value of the outlier, and the row in the worksheet that contains the outlier.

## Step 3: Visually identify the outlier

Use the outlier plot to visually identify an outlier in the data. If an outlier exists, Minitab represents it on the plot as a red square. Try to identify the cause of any outliers. Correct any data–entry errors or measurement errors. Consider removing data values for abnormal, one-time events (also called special causes). In this outlier plot, the smallest value, 12.38, is an outlier.
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