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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In two previous post (1, 2), I highlighted a symposium that was held to improve the reproducibility of biomedical research. The published report includes a discussion on cultural factors that have contributed to the high prevalence of irreproducible research. Culture and nature of science Whether or not the questionable research practices described in the previous post are the result of
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In a previous post, I highlighted a symposium that was held to improve the reproducibility of biomedical research. The published report includes a description of the causes and factors associated with poor reproducibility; these are summarized below. Key causes and factors linked to poor reproducibility False discovery rate and small sample sizes. The false discovery rate is the expected proportion
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In April 2015, a symposium was held on how to improve the reproducibility of biomedical research. It was attended by scientists from research institutions, funding bodies and scientific journals. A report summarizing the symposium was published in November of last year. A very readable document, the report discusses various factors that contribute to poor reproducibility, as well as various recommendations
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Science is about asking questions, getting data and (often) applying statistical methods to use data to answer questions. What are some principles of effective statistical practice that statisticians would like working scientists to know? In the ongoing “Ten Simple Rules” series at PLoS Computational Biology, statisticians Kass and colleagues (2016) present some good advice and guidance. Here is a summary
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Note. This is a semi-independent summary of the same paper that Marty wrote about in the post here. I wrote it to present a different perspective on an otherwise fascinating idea. There is an increasing awareness and deep concern that most scientific findings are not reliable or valid. Many key researchers and research groups have called for (1) greater transparency
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Scientifically Sound 