Category Archives: Research tools and methods

The Open Science Framework, Part 2: adding files and contributors

In the previous post we learned how to create a project folder and pre-register a study protocol on the Open Science Framework (OSF). The OSF also serves as a good data repository to store data, and users can add contributors (i.e. collaborators) to projects. How is this done? To add files to a project, navigate to the OSF website, sign

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The Open Science Framework, Part 1: pre-registering a study protocol

Pre-registration of study protocols increases research transparency by providing a time-stamped record of experimental or analysis decisions before studies are conducted. Protocol pre-registration is now mandatory or strongly encouraged for clinical trials, and is increasingly encouraged for basic science and pre-clinical research (e.g. see the Transparency and Openness Promotion Guidelines). The Open Science Framework (OSF) is an open source software

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What is an exact test? An example using Fisher’s Exact test

Suppose you conduct a study to compare an outcome between healthy people and people with stroke, but you want to check whether the proportion of men and women in each group are similar. The Fisher’s Exact test is often used to test differences in proportions between groups, similar to a Chi-Square test. But what is an exact test? This helpful

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More on regression to the mean

Previously, we saw how regression to the mean can lead to false results. In a talk titled Regression towards the mean, or Why was Terminator III such a disappointment?, statistician Martin Bland explains this phenomenon and how it appears in different examples. The Victorian statistician Francis Galton measured the heights of parents and children and found that taller parents tended

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Reflections on p-values and confidence intervals

When we run a statistical test, we almost always obtain a p-value. Many statistical tests will also generate a confidence interval. Unfortunately, many scientists report the p-value and ignore the confidence interval. As pointed by Rothman (2016) and the American Statistical Association, relying on p-values forces a false dichotomy between results that are significant and those that are non-significant. This

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