In conventional DBEF, the observed numeral distribution is compared to a distribution that is hypothesised to have generated the results if there were no irregularities. The comparison takes the form of a statistical significance test of the statistical null hypothesis that the data were drawn from that hypothesized distribution. Null hypothesis significance testing (NHST) is a workhorse of applied statistics and has been fruitfully applied to problems in a very broad variety of domains. Applying NHST generally entails computing a “test statistic” from the data, and comparing its observed value to the distribution of values the test statistic would have if it were applied to many samples of the same size, on the assumption that the null hypothesis is true. This comparison is summarized as a p-value, a measure of how surprising the observed data would be if the null hypothesis were true. (Smaller p-values are stronger evidence that the null hypothesis is false.)