In the first and second post of this series, we learned how to graph our data using histograms and Q-Q plots to see whether it is normally distributed, and quantify the shape of the distribution by considering skew and kurtosis. In this, the final post in this series, we will learn to use the Shapiro-Wilk test to determine whether data
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In our previous post, we learned how to inspect whether or data were normally distributed or not using plots. It is always important to visualise our data. However, inspecting such plots is open for interpretation and, possibly, abuse. We will now learn how to analyse our data and generate numerical values that describe how our data are distributed. Quantifying the
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Many statistical tests assume that the sampling distribution is normally distributed. This does not mean that the data we collected for our experiment is normally distributed, but rather that the distribution of mean values from many samples of the same size will be normally distributed. Unfortunately, we do no have access to the sampling distribution. However, based on the central
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When we run a statistical test, we almost always obtain a p-value. Many statistical tests will also generate a confidence interval. Unfortunately, many scientists report the p-value and ignore the confidence interval. As pointed by Rothman (2016) and the American Statistical Association, relying on p-values forces a false dichotomy between results that are significant and those that are non-significant. This
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Bias is everywhere, and as scientists we must protect ourselves from it at every turn. One area that is particularly prone to the influence of bias is data analysis. As humans we see patterns everywhere, even when they are not even there! As scientists, there is the added pressure–conscious or not–that we should find a result and if at all
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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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