- The distribution is based on data from 500 randomly sampled drivers. The sample size figures into the test statistic below.

- The Poisson distribution is indexed by only one parameter--the mean. This sample of drivers averaged about 1.72 accidents over the past five years.

- The next three rows fall under the general category Most Extreme Differences. The differences referred to are the largest positive and negative points of divergence between the empirical and theoretical CDFs.

- The first difference value, labeled Absolute, is the absolute value of the larger of the two difference values printed directly below it. This value will be required to calculate the test statistic.

- The Positive difference is the point at which the empirical CDF exceeds the theoretical CDF by the greatest amount.

- At the opposite end of the continuum, the Negative difference is the point at which the theoretical CDF exceeds the empirical CDF by the greatest amount.

- The Z test statistic is the product of the square root of the sample size and the largest absolute difference between the empirical and theoretical CDFs.

Unlike much statistical testing, a significant result here is bad news. The probability of the Z statistic is below 0.05, meaning that the Poisson distribution with a parameter of 1.72 is not a good fit for the number of accidents within the past five years in this sample of drivers.