Contributing to journal peer review is a good way to observe and mitigate the research conducted in a scientific field, and contribute to the growth of knowledge. I have peer reviewed for some years, and assessing manuscripts for publication now comes more easily. As a peer reviewer, I think it curious how simple statistical oversights are common at submission. As
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I am used to confused interactions, likely because I get along better with dogs and kids than I do with adults. But that is not what Nieuwenhuis et al. had in mind when they surveyed 513 articles relating to behavior, cognitive function or brain imaging from leading journals (Nature, Science, Nature Neuroscience, Neuron, Journal of Neurosience). What they were looking
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When we try to interpret findings from a study, we often like to understand whether an effect (of a treatment or test condition) might be different in subjects with different characteristics. If there was substantial variability among subjects, this may have masked a treatment effect in a select few. How can we understand effects in select groups of subjects that
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T-tests and analysis of variance (ANOVA) are common statistical tests in physiology and biomedical science. While the SAMPL guidelines for reporting statistical analyses and methods in published literature state authors should “describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to verify the reported results”, such recommendations are rarely implemented. Simply stating
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In previous posts, we learned that the aim of statistics is to extrapolate properties of samples to make inferences about population. However, random variation in individuals in the population produces sampling error, which means a single sample may not accurately reflect properties of the population. When data are binary, we learned how the 95% confidence interval (CI) of a sample
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At Scientifically Sound, we have shown how to verify whether data are Normally distributed, and discussed whether it matters that data are Normally distributed. Let’s take a step back and consider what a Normal distribution is. A Normal distribution is a bell-shaped curve observed when the number of data points that occur in a population (y-axis) is plotted against the
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