The neuroscience review cited in this blog post is originally from nature reviews [0]. While I agree with the general notion that neuroscientists need to get be

The neuroscience review cited in this blog post is originally from nature reviews [0]. While I agree with the general notion that neuroscientists need to get better at using statistical methods and tools, I wonder if the author of the blog you linked (Zalocusky) maybe makes the problem out to be somewhat different than as it is described in the original review. Specifically Zalocusky is trying to link the neuroscience results to an earlier article by Ioannidis [1]. Whereas article [0] makes the conclusion that neuroscience results are unreliable and hard to reproduce, Zalocusky is applying the older methods and results from [1] that this means neuroscience results are not only unreliable but also false, which is a stronger statement. To support this, Zalocusky adds in a couple of back-of-the-napkin calculations. I think a bit more analysis and caution is necessary before making the leap from Ioannidis' 2013 claim about neuroscience research to Zalocusky's stronger claim about neuroscience research. Incidentally there was a lot of discussion about Ioannidis' 2005 paper, some which can be seen by Ioannidis' 2007 response to earlier criticism of the paper [2]. When evaluating claims about entire fields of research, it is important to be careful about how we interpret these claims. edit: We need to be especially careful when using the word false. Does that mean "not true"? Or "insufficient to describe the truth"? Or "directly opposite to the truth"? [0] http://www.nature.com/nrn/journal/v14/n5/full/nrn3475.html http://www.nature.com/nrn/journal/v14/n5/full/nrn3475.html [1] http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020124 http://www.plosmedicine.org/article/info:doi/10.1371/journal... [2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1896210/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1896210/