Science is about asking questions, getting data and (often) applying statistical methods to use data to answer questions. What are some principles of effective statistical practice that statisticians would like working scientists to know? In the ongoing “Ten Simple Rules” series at PLoS Computational Biology, statisticians Kass and colleagues (2016) present some good advice and guidance. Here is a summary
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I am assuming you have followed the previous tutorials in this short series on how to manipulate Pubmed references using Python (1, 2, 3). You should have a list variable called refs4 in your Python console that contains the cleaned references. Because we will be working with this cleaned version of the references from now on, it would be a
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Note. This is a semi-independent summary of the same paper that Marty wrote about in the post here. I wrote it to present a different perspective on an otherwise fascinating idea. There is an increasing awareness and deep concern that most scientific findings are not reliable or valid. Many key researchers and research groups have called for (1) greater transparency
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As stated in a recent post, the majority of scientific findings are likely false (Ioannidis, 2005). Although there are multiple reasons for this, it has been suggested that the pressure to publish is the driving incentive behind scientists adopting poor research practices that lead to false findings. In a recent paper, Smaldino & McElreath (2016) argue that the pressure to
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Researchers often use statistical tests to test hypotheses and/or infer properties of a population based on properties of a sample. A key idea is that all statistical tests assume a statistical model provides a complete and valid representation of variability in the data, and faithfully reflects how the study was conducted and the phenomena being tested. For example, when we
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Scientifically Sound 