Studies in conservation genetics often attempt to determine genetic differentiation between two or more
temporally or geographically distinct sample collections. Pairwise p-values from Fisher’s exact tests or
contingency Chi-square tests are commonly reported with a Bonferroni correction for multiple tests. While
the Bonferroni correction controls the experiment-wise a, this correction is very conservative and results in
greatly diminished power to detect differentiation among pairs of sample collections. An alternative is to
control the false discovery rate (FDR) that provides increased power, but this method only maintains
experiment-wise a when none of the pairwise comparisons are significant. Recent modifications to the FDR
method provide a moderate approach to determining significance level. Simulations reveal that critical
values of multiple comparison tests with both the Bonferroni method and a modified FDR method
approach a minimum asymptote very near zero as the number of tests gets large, but the Bonferroni
method approaches zero much more rapidly than the modified FDR method. I compared pairwise significance
from three published studies using three critical values corresponding to Bonferroni, FDR, and
modified FDR methods. Results suggest that the modified FDR method may provide the most biologically
important critical value for evaluating significance of population differentiation in conservation genetics.
Ultimately, more thorough reporting of statistical significance is needed to allow interpretation of
biological significance of genetic differentiation among populations.
Studies in conservation genetics often attempt to determine genetic differentiation between two or more
temporally or geographically distinct sample collections. Pairwise p-values from Fisher’s exact tests or
contingency Chi-square tests are commonly reported with a Bonferroni correction for multiple tests. While
the Bonferroni correction controls the experiment-wise a, this correction is very conservative and results in
greatly diminished power to detect differentiation among pairs of sample collections. An alternative is to
control the false discovery rate (FDR) that provides increased power, but this method only maintains
experiment-wise a when none of the pairwise comparisons are significant. Recent modifications to the FDR
method provide a moderate approach to determining significance level. Simulations reveal that critical
values of multiple comparison tests with both the Bonferroni method and a modified FDR method
approach a minimum asymptote very near zero as the number of tests gets large, but the Bonferroni
method approaches zero much more rapidly than the modified FDR method. I compared pairwise significance
from three published studies using three critical values corresponding to Bonferroni, FDR, and
modified FDR methods. Results suggest that the modified FDR method may provide the most biologically
important critical value for evaluating significance of population differentiation in conservation genetics.
Ultimately, more thorough reporting of statistical significance is needed to allow interpretation of
biological significance of genetic differentiation among populations.
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