Forum Comparing effect sizes across variables: generalization without the need for

Studies in behavioral ecology often investigate several traits and then apply multiple statistical tests to discover their pair-wise associations. Traditionally, such approaches require the adjustment of individual significance levels because as more statistical tests are performed the greater the likelihood that Type I errors are committed (i.e., rejecting H0 when it is true) (Rice 1989). Bonferroni correction that lowers the crit-ical P values for each particular test based on the number of tests to be performed is frequently used to reduce problems associated with multiple comparisons (Cabin and Mitchell 2000). However, this procedure dramatically increases the risk of committing Type II errors as it results in a high risk of not rejecting a H0 when it is false. To reach 80 % statistical power, it is necessary to have huge sample sizes to detect medium (r 0.3 or d 0.5; sensu Cohen 1988) or small (r 0.1 or d 0.2; sensu Cohen 1988) strength effects (e.g., say N 128 or N 788, respectively, for a 2-sample t-test), but sample size is often limited when studying behavior. The strict application of Bonferroni correction in the field of ecology and behavioral ecology has therefore been criti-cized for mathematical and logical reasons (Wright 1992