Category Archives: Research tools and methods

False-positive findings and how to minimize them

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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Calculating sample size using precision for planning

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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The likelihood ratio test: relevance and application

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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Add jitter to your figures using Python and R

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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Calculating sample size for a paired t-test

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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