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When the flawed test is used to compute the significance of this data, the p-value is 0.0085 (see Table 2 of the paper). This p-value is considered statistically significant. However, when the data is analyzed using a standard method (the chi-square test) the p-value is 0.19. This p-value is much larger than the one from the paper and indicates that there is no reason to reject the null hypothesis. The chi-square test p-value for experiment two is small (0.0004). However, experiment three is not statistically significant because its p-value is 0.22.

When the test used by Blount is used to compute the significance of this data, the p-value is 0.0085 (see Table 2 of the paper). This p-value is considered statistically significant, implying that the rate of mutation increases over time. However, when the data is analyzed using a standard method (the chi-square test) the p-value is 0.19. This p-value suggests that the mutation rate is constant over time. Note, however, that the chi-squared test is conservative when any expected value is less than 1 and unreliable when, as in the present case, many expected values are less than 0.5 <ref>B.S. Everitt (1977) ''The Analysis of Contingency Tables''. Chapman & Hall.</ref>. Consequently, while the p-value of 0.19 appears to suggest there is no reason to reject the null hypothesis, it is in fact much too high and the results of the chi-squared test are unreliable.

 

In statistical terms, ''conservative'' means that the p-value is higher than it ought to be, causing the experimenter to accept the null hypothesis when it should in fact be rejected.

The chi-square test is a common statistical method.<ref>''Mathematical Statistics with Applications'' by Wackerly, Mendenhall, and Scheaffer, Section 14.4.</ref> It can be implemented in Microsoft Excel. If the numbers from the last four columns of the experiment one data table (excluding the “totals” row) are entered into Excel in rows 1-12 and columns A-D, then the p-value can be computed by entering “=CHITEST(A1:B12,C1:D12)” into any empty cell of the spreadsheet.

The chi-square test is a common statistical method.<ref>''Mathematical Statistics with Applications'' by Wackerly, Mendenhall, and Scheaffer, Section 14.4.</ref> It can be implemented in Microsoft Excel. If the numbers from the last four columns of the experiment one data table (excluding the “totals” row) are entered into Excel in rows 1-12 and columns A-D, then the p-value can be computed by entering “=CHITEST(A1:B12,C1:D12)” into any empty cell of the spreadsheet.