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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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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I have completed undergraduate level research, a Master’s degree, a PhD, and two post-docs, yet I have never been taught or told how I should organize the files and folders related to a project. Being organized is essential in most aspects of life, and this is particularly trying when conducting research. I only found a few online examples of blogs
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At Scientifically Sound, we have reviewed ongoing discussions on the benefits of confidence intervals (CIs) over p values for statistical analysis and reproducibility in research. In a short editorial, the statistician Doug Altman summarised why we need confidence intervals and showed how confidence intervals force investigators to consider sizes of effects. Here are the key points: Two different but complementary
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The growing awareness of the need for reproducibility in research is encouraging, but what does reproducible research actually look like in practice? Marty and I recently had an interesting discussion on what it means for a study’s findings to be independently replicated, and the metrics scientists use to interpret reproducibility. I tend to interpret level of reproducibility using confidence intervals
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