Most sample size calculations for independent or paired samples are performed based on power to detect an effect of a certain size, assuming there’s no effect. Instead, Cumming and Calin-Jageman recommend that readers plan studies to detect precise effects. The 95% confidence interval (CI) indicates precision about effects. Therefore, it is possible to plan studies to detect narrow 95% CIs
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Suppose you conduct a study to compare an outcome between two independent groups of people, but you realised later that the groups were unexpectedly different at baseline. This difference might affect how you interpret the findings. For example, you measured muscle stiffness in people with stroke and in healthy people. At the end of the study, you realised that on
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Suppose you are planning to conduct a repeated-measures study, where outcomes are measured from the same subject at more than one point in time and the average within-subject effect is calculated using a paired t-test or linear regression. How might you calculate how many subjects need to be tested in order to find an effect? Similar to calculating sample size
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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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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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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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