Most introductory statistics courses include a section explaining Type I (false positive) and Type II (false negative) errors in hypothesis testing. If you have been through such courses, you would have learned that the tolerance for Type I error is set by the significance level (alpha =0.05; the usual default) while Type II error is controlled by statistical power which
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Academics and researchers must apply for grant funding. These grants provide the financial support required to carry out the research proposed in the grant. These funds are used to purchase equipment, pay salaries, cover publication and travel costs, etc. Being successful in a national funding competition is a key indicator of success. Not only can the researchers carry out the
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Academics and scientists, especially those in administrative positions looking to rate their peers/staff/candidates, have traditionally used impact factors as an indicator of quality and influence. Only the best, most trustworthy and innovative research gets published in journals with high impact factors. At least, that is what they would like us to believe… What is an impact factor? The impact factor
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The pressure to publish and current incentives that reward highly-cited discoveries leads to research findings that are not reproducible and inadvertently results in the natural selection of bad science. It is difficult to encourage scientists to take effort in conducting reproducible and rigorous research without better incentives. What kinds of metrics and incentives might reward scientists for conducting sound science?
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Researchers love p-values, especially when they are significant. It has previously been demonstrated that there is a disproportionate number of positive or significant p-values. In other words, many reported p-values must be false. That is to say, they are associated with false-positive findings. In a recent article, Cristae & Ioannidis (2018) investigated the characteristics of p-values reported in figures and
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Many people learn to write computer code by attending short courses, or learning off supervisors or colleagues, then figure things out as they go. As beginner or novice programmers, it is not always easy to work out whether code is well documented or efficient. So it is nice when more experienced users offer advice on good coding practices. A recent
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