Most introductory statistics courses include a section explaining Type I (false positive) and Type II (false negative) errors in hypothesis testing. If you have been through such courses, you would have learned that the tolerance for Type I error is set by the significance level (alpha =0.05; the usual default) while Type II error is controlled by statistical power which
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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 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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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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