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In community ecology, randomization tests with problem specific test statistics (e.g., nestedness, functional diversity, etc.) are often applied. Researchers in such studies may want not only to detect the significant departure from randomness, but also to measure the effect size (i.e., the magnitude of this departure). Measuring the effect size is necessary, for instance, when the roles of different assembly forces (e.g., environmental filtering, competition) are compared among sites. The standard method is to calculate standardized effect size (SES), i.e., to compute the departure from the mean of random communities divided by their standard deviations. Standardized effect size is a useful measure if the test statistic (e.g., nestedness index, phylogenetic or functional diversity) in the random communities follows a symmetric distribution. In this paper, I would like to call attention to the fact that SES may give us misleading information if the distribution is asymmetric (skewed). For symmetric distribution median and mean values are equal (i.e., SES = 0 indicates p = 0.5). However, this condition does not hold for skewed distributions. For symmetric distributions departure from the mean shows the extremity of the value, regardless of the sign of departure, while in asymmetric distributions the same deviation can be highly probable and extremely improbable, depending on its sign. To avoid these problems, I recommend checking symmetry of null-distribution before calculating the SES value. If the distribution is skewed, I recommend either log-transformation of the test statistic, or using probit-transformed p-value as effect size measure.

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Interventional Medicine and Applied Science
Authors: Sirous Azizi, Afsaneh Dadarkhah, Zahra Rezasoltani, Seyed Ahmad Raeissadat, Reza Kazempoor Mofrad, and Sharif Najafi

based on the ability to detect a large Cohen’s standardized effect size of 0.9, a power of 80%, and two-tailed p value of 0.05 as statistically significant. To minimize potential recruitment bias, we collected baseline data before

Open access
Interventional Medicine and Applied Science
Authors: Sirous Azizi, Afsaneh Dadarkhah, Zahra Rezasoltani, Seyed Ahmad Raeissadat, Reza Kazempoor Mofrad, and Sharif Najafi

sample size of 16 participants in each group based on the ability to detect a large Cohen’s standardized effect size of 0.9, a power of 80%, and two-tailed p value of 0.05 as statistically significant. To minimize potential recruitment bias

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. Sample size, randomization, and statistical analyses Based on the power of 80% and a two-tailed p value of less than 0.05 as statistically significant, we considered 30 participants in each group to find Cohen’s moderate standardized effect

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. Sample size, randomization, and statistical analyses Based on the power of 80% and a two-tailed p value of less than 0.05 as statistically significant, we considered 30 participants in each group to find Cohen’s moderate standardized effect

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; therefore, the larger the standard error, the larger the effect size necessary to get a study published. Begg and Mazumdar’s correlation estimates this by computing the rank correlation (Kendall’s τ) between standardized effect sizes and their variances

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