# Interpret the key results for Goodness-of-Fit Test for Poisson

Complete the following steps to interpret a goodness-of-fit test for Poisson. Key output includes the p-value and several graphs.

## Step 1: Determine whether the data do not follow a Poisson distribution

To determine whether the data do not follow a Poisson distribution, compare the p-value to your significance level (α). 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 the data do not follow a Poisson distribution when the data do follow a Poisson distribution.
P-value ≤ α: The data do not follow a Poisson 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 a Poisson distribution.
P-value > α: You cannot conclude that the data do not follow a Poisson 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 a Poisson distribution.

## Step 2: Examine the difference between observed and expected values for each category

Use a bar chart of observed and expected values to determine whether, for each category, the number of observed values differs from the number of expected values. Larger differences between observed and expected values indicate that the data do not follow a Poisson distribution.