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.
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.
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.
The descriptive statistic is often the first step in the analysis of data. It analyzes data in columns (or rows) with several measures to describe central tendency (mean, median), scatter and normality. Learn more about it in our latest lesson.
The detection and decision to remove outliers from a data set can be very tricky. Learn about outliers, their sources and the Grubbs test to detect real outliers, and not just removing maximum and minimum.
Read our fourth lesson to learn about the difference between parametric and non-parametric tests.
Learn the concept of null hypothesis and p value. The p value is important for the interpretation of results from t-tests and ANOVA. essential statistical tests to verify hypotheses.