Interpret the key results for Probability Plot

Complete the following steps to interpret a probability plot.

Step 1: Determine whether the data do not follow the specified distribution

To determine whether the data do not follow the specified theoretical distribution, 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 that the risk of concluding the data do not follow the specified distribution—when, actually, the data do follow the specified distribution—is 5%.
P-value ≤ α: The data do not follow the specified distribution (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 your data do not follow the specified distribution.
P-value > α: Cannot conclude the data do not follow the specified distribution (Fail to reject H0)
If the p-value is larger than the significance level, the decision is to fail to reject the null hypothesis because you do not have enough evidence to conclude that your data do not follow the specified distribution. However, you cannot conclude that the data do follow the specified distribution.
Anderson-Darling Test
AD-Value
P-Value
Key Result: P-Value

For example, in the following results, the null hypothesis states that the data follow a normal distribution. Because the p-value is 0.4631, which is greater than the significance level of 0.05, the decision is to fail to reject the null hypothesis. You cannot conclude that the data do not follow a normal distribution.

Step 2: Visualize the fit of the specified distribution

Examine the probability plot and assess how closely the data points follow the fitted distribution line. If the specified theoretical distribution is a good fit, the points fall closely along the straight line. For example, the points in the following normal probability plot follow the line well. The normal distribution appears to be a good fit to the data.

Step 3: Display estimated percentiles for the population

Hold the pointer over the fitted distribution line to see a chart of percentiles and values.

For example, the following probability plot shows the pulse rates of test subjects as they walked on a treadmill. For a normal distribution with a mean and standard deviation equal to the data, you would expect 5% of the population to have a pulse rate of 54.76 or less.

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