In our two previous post on Cohen’s d and standardized effect size measures [1, 2], we learned why we might want to use such a measure, how to calculate it for two independent groups, and why we should always be mindful of what standardizer (i.e., the denominator in d = effect size / standardizer) is used to calculate Cohen’s d.
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Scientists often plan for studies by calculating how many subjects or units need to be tested in order to find an effect. That is, they plan for a study using statistical power according to principles of hypothesis testing. Sample size calculations are usually required in ethics applications and grant proposals to justify the study. We previously learned how to calculate
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In a previous post we learned about Cohen’s d, a standardized measure of effect size. In this post we will learn why it is important to consider what value is being used to standardize our effect size. Cohen’s d and the standardiser The basic formula to calculate Cohen’s d is: d = [effect size / relevant standard deviation]. The denominator
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Reporting bias (ie. bias arising when dissemination of research findings is influenced by the results) is thought to be common in biomedical and medical research. However, exactly how common it is has been difficult to quantify. Albarqouni and colleagues examined how commonly reporting bias occurs by examining the distribution of p values in medical research studies, and compared these distributions
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Scientifically Sound 