The dark side of competition in science

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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Reproducible research practices are underused in systematic reviews of biomedical interventions

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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False-positive findings and how to minimize them

As scientists we collect data and look for patterns or differences. Because populations display variation and we are unable to collect data from all members of a population, statistical results will always possess a level of uncertainty. For example, it is common to set alpha to 0.05. This implies that if there is no difference or effect, there is a

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The Critical thinking and Appraisal Resource library (CARL) to understand and assess treatment claims

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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Implying “there’s a trend to statistical significance” is not trendy.

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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