For science to move forward, it is essential that key research findings be reproducible. However, current publication and career progression models do not value replication. It is almost guaranteed that a replication study will be published in a journal with a lower impact factor than the journal where the original study was published. As pointed out by McLoughlin & Drummond
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Reporting bias (ie. bias arising when dissemination of research findings is influenced by the results) is thought to be common in biomedical and medical research. However, exactly how common it is has been difficult to quantify. Albarqouni and colleagues examined how commonly reporting bias occurs by examining the distribution of p values in medical research studies, and compared these distributions
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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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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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Scientifically Sound was born out of the need to provide an online resource to conduct science that is reproducible and valid. This resource is in recognition of the fact that learning the digital skills to manage and analyse data reliably takes time and can be daunting. These concerns were echoed in the recent Nature article Scientific computing: Code alert. It
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As a new programmer, it still takes me a while to master a language and implement good coding practices. Novice and experienced programmers recognise the difficulties in learning to write code that is generalisable enough to be used in future projects, and by other users. To address these difficulties, Morgan Taschuk and Greg Wilson offer some simple tips to make
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