Staticman is an API that can be used to implement a commenting system for static websites. Here, I discuss how I managed to set up my own instance of the Staticman API and how it can be integrated into a Hugo site.
Bar plots are frequently used due to their simplicity. However, they also do not convey a lot of information. Here, I discuss how error bars can be used to visualize variance and under which circumstances bar charts should not be used.
Box plots are ideal for showing the variation of measurements because they do not only visualize the first, second, and third quartile, but also outliers.
Since R was made for statistical computations, it is very easy to deal with distributions in R. Since there are multiple functions for each distribution, I exemplify their application using the normal distribution.
Scatter plots are a great tool for learning about individual data points. Here, I demonstrate the use of scatter plots for visualizing the correlation between two variables.
Histograms are an ideal tool for visualizing the distribution of a variable and frequently used for data exploration. Here, I show how a histogram acan aid in differentiating two distributions.
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.