Scaling of data: One of the properties of the tests is the scale of the data, which can be
interval-based,
ordinal or
nominal.[3] Nominal scale is also known as categorical.[6] Interval scale is also known as numerical.[6] When categorical data has only two possibilities, it is called
binary or
dichotomous.[1]
Assumptions, parametric and non-parametric: There are two groups of statistical tests,
parametric and
non-parametric. The choice between these two groups needs to be justified. Parametric tests assume that the data follow a particular distribution, typically a
normal distribution, while non-parametric tests make no assumptions about the distribution.[7] Non-parametric tests have the advantage of being more resistant to misbehaviour of the data, such as
outliers.[7] They also have the disadvantage of being less certain in the statistical estimate.[7]
Type of data: Statistical tests use different types of data.[1] Some tests perform
univariate analysis on a single sample with a single variable. Others compare two or more
paired or unpaired samples. Unpaired samples are also called independent samples. Paired samples are also called dependent. Finally, there are some statistical tests that perform analysis of relationship between multiple variables like
regression.[1]
Number of Samples: The number of samples of data.
Exactness: A test can be
exact or be
asymptotic delivering approximate results.
List of statistical tests
This section needs expansion. You can help by
adding to it. (February 2024)