Category Archives: Research tools and methods

Research funding: let’s play the lottery!

Last year, Fang & Casadevall (2016) wrote an editorial entitled Research Funding: the Case for a Modified Lottery highlighting the chronic and severe lack of research funds and the resistance to change how these funds are allocated. While their proposal may seem drastic, especially to the lucky 10% or so who get their grants funded, the arguments put forth by

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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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The difference between allocation concealment and blinding in randomised controlled trials

Allocation concealment and blinding are characteristics that prevent bias in randomised controlled trials and experimental studies. However, these concepts are often confused. Using a randomised controlled trial as an example, the statistician Philip Sedgwick explains the differences between allocation concealment and blinding, and why these characteristics are important: Researchers investigated whether a nutritious meal and food packages was more effective

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Adjusting for differences at baseline in controlled trials

In randomised trials or repeated-measures experimental studies of randomised conditions, researchers often measure a continuous variable at baseline and at the end of the study at follow up. Examples of some outcomes include blood pressure, pain, physiological responses, range of motion, etc. In a BMJ statistics note, methodologists Andrew Vickers and Doug Altman explain how these outcomes can be analysed.

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