Verify if data are normally distributed in R: part 2

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

Read more

Verify if data are normally distributed in R: part 1

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

Read more

Reflections on p-values and confidence intervals

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

Read more
« Older Entries