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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An important part of conducting sound science involves interpreting data correctly. Unfortunately, we don’t do that very well. For example, we are fooled by regression to the mean, we report findings when there are none, and we are overconfident about statistical power and significance. As scientists and lay persons, we want to be certain about research findings. But statistics only
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Many medical and scientific questions are the sort that cannot be answered using randomised study designs. For example, Does cigarette smoking cause lung cancer?, What is the risk hip fracture in adults?, and Do rats indirectly destroy coral reefs? Scientists often use observational cohort studies to answer these types of questions. In cohort studies, (1) a sample of subjects or
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In the first and second posts of this series, we performed simple linear regression of a continuous outcome on a single continuous predictor, but we also learned it is possible to include binary or categorical predictors in such regression models. How is this be done? The hsb2.csv dataset we have been using also contains the variable female where male participants
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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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In the previous post, we performed simple linear regression of science scores on reading scores from 200 students using ordinary least squares (OLS) estimation. This was done using Python’s Statsmodels package. What does the OLS output show and how should it be interpreted? Here is the figure of the individual subject data and the line of best fit, as well
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