As scientists we collect data and look for patterns or differences. Because populations display variation and we are unable to collect data from all members of a population, statistical results will always possess a level of uncertainty. For example, it is common to set alpha to 0.05. This implies that if there is no difference or effect, there is a
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In our previous post, we revisited the Ioannidis argument on Why most published research findings are false. Other factors such as p-hacking can also increase the chance of reporting a false-positive result. Such results are associated with a p-value deemed to be statistically significant, but the underlying hypothesis is in fact false. Researcher degrees of freedom As scientists, we have
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It has been more than a decade that Ioannidis published his paper entiled Why most published research findings are false. Forstmeier et al. (2016) recently revisited the Ioannidis argument, and I thought it worthwhile to prepare a blog post on the topic to cement my understanding. Looking for a novel effect Let’s consider 1000 hypotheses we might want to test.
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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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Scientifically Sound 