Taxonomies in Hugo are a great way to structure information provided by a blog. For search engine optimization (SEO) purposes, however, the existence of duplicate content on a site can be a problem. If you think this is the case for your site, then you can use the noindex meta tag for all of the taxonomy sites that do not provide unique content. This post shows you how to get it done.
McNemar’s test is a non-parametric test for contingency tables that arise from paired measurements. In contrast to the chi-squared test, which is a test for independence, McNemar’s test is a test for symmetry (also called marginal homogeneity). Still, McNemar’s test is related to the chi-squared test because its test static also follows a chi-squared distribution.
Static blogs are a great alternative to dynamic blogs that are based on content management systems such as WordPress. While both approaches have their up- and downsides, I chose the static approach for this blog because it’s the easiest way to bring R code to the web. In the following, I will show how you can create a static blog in only five steps.
In a previous post, I’ve contrasted two tests for pairs of measurements. Here, I’d like to show why it is important to choose a test that appropriately accounts for such dependent measurements.
Parametric tests are subject to assumptions about the properties of the data. For example, Student’s t-test is a well-known parametric test that assumes that sample means have a normal distribution. Due to the central limit theorem, the test can be also applied to measurements that are not normally distributed if the sample size is sufficient. Here, we will investigate the approximate number of samples that are necessary for the t-test to be valid.
One of the most common areas of statistical testing is testing for independence in contingency tables. In this post, I will show how contingency tables can be computed and I will introduce two popular tests on contingency tables: the chi-squared test and Fisher’s exact test.
In this post, we will explore tests for comparing two groups of dependent (i.e. paired) quantitative data: the Wilcoxon signed rank test and the paired Student’s t-test. The critical difference between these tests is that the test from Wilcoxon is a non-parametric test, while the t-test is a parametric test. In the following, we will explore the ramifications of this difference.
