For science to move forward, it is essential that key research findings be reproducible. However, current publication and career progression models do not value replication. It is almost guaranteed that a replication study will be published in a journal with a lower impact factor than the journal where the original study was published. As pointed out by McLoughlin & Drummond
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In a previous post we learned how to use matplotlib’s gridspec to make subplots of unequal size. gridspec is quite powerful, but can be a little complicated to use. This is especially true if you are coming to Python from Matlab. In the current post we learn how to make figures and subplots in a manner that will be more
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Scientific figures are at their most informative when they include the individual data used to calculate summary statistics such as means and standard deviations. Why is showing data important? As previously pointed out here and here, figures with means, standard deviations, standard errors, etc. can be misleading and conceal the nature of the underlying data. As highlighted in our previous
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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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