Tag Archives: reproducibility

Reproducible research practices are underused in systematic reviews of biomedical interventions

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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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Why most published findings are false: the effect of p-hacking

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In our previous post, we revisited the Ioannidis argument on Why most published research findings are false. Other factors such as p-hacking can also increase the chance of reporting a false-positive result. Such results are associated with a p-value deemed to be statistically significant, but the underlying hypothesis is in fact false. Researcher degrees of freedom As scientists, we have

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Why most published findings are false: revisiting the Ioannidis argument

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It has been more than a decade that Ioannidis published his paper entiled Why most published research findings are false. Forstmeier et al. (2016) recently revisited the Ioannidis argument, and I thought it worthwhile to prepare a blog post on the topic to cement my understanding. Looking for a novel effect Let’s consider 1000 hypotheses we might want to test.

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The Conversation: Seven deadly sins of statistical misintepretation, and how to avoid them

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The Conversation recently published a nice piece by Louis and Chapman on common statistical misinterpretations and how they can be avoided. Here is summary of the main points: Problem Reason Solution 1. Assuming small differences are meaningful Most small differences are due to chance, not meaningful differences Ask for the margin of error (ie. half of the 95% CI): if

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Sharing computer code and programs in neuroscience

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Many areas of neuroscience require computational techniques to process or analyse data. Some journals including the Nature family of journals and BioMed Central are encouraging investigators to share computer code and programs to improve scientific reproducibility, but these practices are not always adopted. For complex projects with multiple collaborators, it may be unclear what or how much can be shared.

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