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Saskia le Cessie, Jelle J Goeman, and Olaf M Dekkers

When statistically comparing outcomes between two groups, researchers have to decide whether to use parametric methods, such as the t-test, or non-parametric methods, like the Mann–Whitney test. In endocrinology, for example, many studies compare hormone levels between groups, or at different points in time. Many papers apply non-parametric tests to compare groups. We will explain that non-parametric tests have clear drawbacks in medical research, and, that’s the good news, they are often not necessary.

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Saskia le Cessie, Rolf H H Groenwold, and Olaf M Dekkers

There are different ways to quantify the relation between two or more continuous variables. Some researchers use correlation coefficients; others will apply regression methods such as linear regression. In this paper, we show that the choice between correlation and regression is not purely a statistical one but largely depends on the research aims. Importantly, one should always inspect the data before using either of the two methods.

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Rolf H H Groenwold, Jelle J Goeman, Saskia Le Cessie, and Olaf M Dekkers

In almost all medical research, more than a single hypothesis is being tested or more than a single relation is being estimated. Testing multiple hypotheses increases the risk of drawing a false-positive conclusion. We briefly discuss this phenomenon, which is often called multiple testing. Also, methods to mitigate the risk of false-positive conclusions are discussed.