The chi-square test provides the benchmarks to judge where chance differences end and significant differences begin. A proper null hypothesis for your data says that one day for an abduction is as good as the next, no day or days related to the menstrual cycle finds special favor. The final expected value for each category is this daily rate multiplied by the number of days per category. A comparison of the expected frequencies of events with the actual frequencies you list in your tables comes next. If the patterns fail to match, the alternative hypothesis is true-the probabilities are not identical, not chance, but something else arranges the distribution of events.
Standard tables give values that represent significant cutoff points.
This percentage is an arbitrary mark, and is considered a weak threshold of significance. Expected Residual Chi-square 3.84 1. Expected Residual Chi-square 3.84 1. The tests only reaffirm this impression. My conclusion would have to be that your events follow pretty much the distribution expected by chance.