Experimental studies are often based on relatively small samples. It is always better to test more subjects where possible, but even if the final dataset is comparatively small we should still strive to analyse the data properly. Let’s look at one way to analyse a small dataset. We will analyse data from the sleep dataset available in R. The sleep
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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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Scientists are often interested in understanding the relationship between two variables. One simple way to understand and quantify a relationship between two variables is correlation analysis. Assumptions. This post assumes you understand the theory behind correlation analysis and have a working knowledge of R; it focuses on how to run this type of analysis in R. The dataset: foot length
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Scientifically Sound 