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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Self reflection is important. As a human endeavour with the whole of society as its beneficiary, science should also have introspection; looking in on itself to identify things worth celebrating, as well as things that need improvement. In a recent article, Ioannidis explains why we need meta-research: research on research. Meta-research The goal of meta-research is to understand and improve
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In a previous post, we profiled the EQUATOR network and reporting guidelines. These guidelines stress transparency in reporting study methods, and most are relevant to study designs in clinical research, such as randomised controlled trials, epidemiological studies and systematic reviews. Taking a different vein, the Transparency and Openness Promotion (TOP) Guidelines were developed to enhance transparency in reporting of study
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Statistics are not broken. The problem lies with how scientists use, interpret and report them. At the end of 2017, Nature asked five influential statisticians to recommend a key change that would improve science. Adjust for human cognition The first change was proposed by Jeff Leek of the Johns Hopkins School of Public Health, and it is based on the
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As scientists, we have all received some level of training in statistics. A fundamental concept is that we are trying to make inferences about a specific population, but that we only have access to a sample of the people, dogs, amoebas, etc that belong to that population. By randomly sampling amoebas for example, we collect data and conduct statistical tests
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