Earlier this year, I was asked to review a manuscript for the Journal of Neurophysiology. I was struck by the use of the standard error of the mean (SEM) to summarize data variability, the selective reporting of exact (e.g., p=0.067) and non-exact (p<0.05) p-values and the interpretation of non-significant results (e.g., p=0.067) as statistically significant. Because the Journal of Neurophysiology
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Every scientist has, at one point or another in their career, plotted results using bar graphs or dot plots with error bars. As pointed in a previous post, these kinds of summary graphs can be misleading, especially since a depressingly large number of scientists plot their error bars as the standard error of the mean (SEM) rather than the standard
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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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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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Sometimes we would like to focus more on some data and less on others, but still provide a visual display. The matplotlib function gridspec allows subplots of unequal size to be plotted on the same figure. How this function can be applied will be demonstrated using simulated data. Let’s simulate some common probability distributions of different statistics using Python’s numpy.random
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In hypothesis testing, the number of samples needed to find an effect depends on the: size of the effect variability of the effect tolerance to reporting a false positive (Type I error) and probability of not reporting a false negative (1 – probability of Type II error) The following code uses the stats package in R to calculate sample size
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