Category Archives: Research tools and methods

Calculating sample size for a 2 independent sample t-test

Scientists often plan for studies by calculating how many subjects or units need to be tested in order to find an effect. That is, they plan for a study using statistical power according to principles of hypothesis testing. Sample size calculations are usually required in ethics applications and grant proposals to justify the study. We previously learned how to calculate

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Cohen’s d: a standardized measure of effect size

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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Why we need confidence intervals

At Scientifically Sound, we have reviewed ongoing discussions on the benefits of confidence intervals (CIs) over p values for statistical analysis and reproducibility in research. In a short editorial, the statistician Doug Altman summarised why we need confidence intervals and showed how confidence intervals force investigators to consider sizes of effects. Here are the key points: Two different but complementary

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How are confidence intervals useful in understanding replication?

The growing awareness of the need for reproducibility in research is encouraging, but what does reproducible research actually look like in practice? Marty and I recently had an interesting discussion on what it means for a study’s findings to be independently replicated, and the metrics scientists use to interpret reproducibility. I tend to interpret level of reproducibility using confidence intervals

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