Statistics are not broken. The problem lies with how scientists use, interpret and report them. At the end of 2017, Nature asked five influential statisticians to recommend a key change that would improve science. Adjust for human cognition The first change was proposed by Jeff Leek of the Johns Hopkins School of Public Health, and it is based on the
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As scientists, we have all received some level of training in statistics. A fundamental concept is that we are trying to make inferences about a specific population, but that we only have access to a sample of the people, dogs, amoebas, etc that belong to that population. By randomly sampling amoebas for example, we collect data and conduct statistical tests
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Competition in science is ever increasing. Research funds are harder to come by and positions have increasing number of applicants. Being the first to publish a result has a disproportionately large impact on prestige and advancement. These problems were already present a decade ago when Anderson et al. (2007) published their paper entitled The perverse effects of competition on scientists’
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Researchers are increasingly encouraged to implement reproducible research practices in their work. These practices include describing the data collected and used for analysis in detail, clearly reporting the analysis method and results, and sharing the dataset and statistical or analysis code. To determine how well reproducible research practices are implemented, Page and colleagues (2017) investigated their implementation in systematic reviews
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Every day, we are confronted by claims about effects of treatments, many of which are not supported by evidence and are misleading. It is easy to overestimate the benefits of treatments and to underestimate their potential risks, without knowing how to accurately assess claims about treatments. To address these problems, Castle and colleagues developed the Critical thinking and Appraisal Resource
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When a p value that fails to reach a threshold is reported, investigators sometimes imply there is a “trend towards statistical significance”. This interpretation expresses the view that if more subjects had been tested, the p value would have become more significant. Epidemiologists Wood and colleagues examined the probability of how the p value of a treatment effect changes when
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