In a previous post, we saw that the number of subjects or samples in our study does not influence the rate of false-positive findings. In this post we will learn how sample size influences estimates of the size of studied effects. If the true effect of a medication is to reduce heart rate by 10 beats per minute, how well
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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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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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Various tools, scales and techniques are available to researchers to quantify outcome measures. Some of these tools are familiar, like a weight scale to measure weight loss over the course of an exercise program. Others are less familiar and are only understood by those working in the same field. Furthermore, different outcome measures can be calculated from the same data.
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