Part 9 (Hypothesis testing): t-tests and nonparametric tests

We are moving on in our course to statistical hypothesis testing, including t-tests and ANOVA. They are commonly used in statistics, but as a non-statistician it can be difficult to select the right one. Here we describe the t-tests and non-parametric equivalents, so you can learn, which one to use for your data analysis.


Part 8 (Basic Statistics): Outlier detection

It is not appropriate to remove data from a group simply because they seems to someone to be unreasonably extreme. However, if with proper testing and scientific reasoning the data appear to be incorrect, they should be eliminated from the group. In lesson 8, we introduce the Grubbs test to detect outliers.

Part 7 (Basic Statistics): Normality

In statistics, we use normality tests to determine whether a data set follows a normal distribution or not, or to compute how likely an underlying random variable is to be normally distributed. We have already learned that with the choice between parametric and non-parametric tests it is very important to know whether our data follows a normal distribution or not. In this lesson you learn which statistical tests you can apply to check normality of your data.