If your data follows a normal distribution using the mean is fine. But what should you do in other cases? Here, I explore the implications of using one or the other measure.
To compare the statistical significance of multiple quantitative variable, the ANOVA test is the way to go. Here, I discuss what you should consider when performing an ANOVA in R.
When designing and performing statistical tests it is important to think about type 1 and type 2 errors. In this post, I investigate the impact of the two error types on significance and power, respectively.
Effect sizes are often overlooked in favor of significance. Here, you will learn why effect sizes are important and how they can be computed using R.
If your site is not structured well, you may have a lot of duplicate content that can result from taxonomies (e.g. tags/category pages). Here, I show how this problem can be solved by setting the noindex meta tag for Hugo taxonomy pages.
McNemar's test is a simple test for for checking whether pairwise measurements from two categories are independent. Here, I investigate the properties of the test and how it is used in R.
Do you want to create a blog? It has never been easier than using Hugo, a static website generator. This post explores how it is possible to have a website up and running in only 5 simple steps.
Measurements often come in pairs. Here I discuss what can go wrong when performing statistical tests that do not take this structure into account.
Parametric tests require that data are normally distributed. Here, you will learn how many samples are necessary to satisfy the assumptions of parametric tests.
Testing whether two groups are independent of each other is a common use case for the Chi-squared and Fisher's exact test. But, under which conditions are these tests appropriate?