Types of Statistical Tests

Now that you have looked at the distribution of your data and perhaps conducted some descriptive statistics to find out the mean, median or mode, it is time to make some inferences about the data. As previously covered in the module, inferential statistics are the set of statistical tests we use to make inferences about data. These statistical tests allow us to make inferences because they can tell us if the pattern we are observing is real or just due to chance.

How do you know what kind of test to use?

Types of statistical tests: There are a wide range of statistical tests. The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. In general, if the data is normally distributed you will choose from parametric tests. If the data is non-normal you choose from the set of non-parametric tests. Below is a table listing just a few common statistical tests and their use

Type of Test:

Use:

Correlational

These tests look for an association between variables

Pearson correlation

Tests for the strength of the association between two continuous variables

Spearman correlation

Tests for the strength of the association between two ordinal variables (does not rely on the assumption of normal distributed data)

Chi-square

Tests for the strength of the association between two categorical variables

Comparison of Means: look for the difference between the means of variables

Paired T-test

Tests for difference between two related variables

Independent T-test

Tests for difference between two independent variables

ANOVA

Tests the difference between group means after any other variance in the outcome variable is accounted for

Regression: assess if change in one variable predicts change in another variable

Simple regression

Tests how change in the predictor variable predicts the level of change in the outcome variable

Multiple regression

Tests how change in the combination of two or more predictor variables predict the level of change in the outcome variable

Non-parametric: are used when the data does not meet assumptions required for parametric tests

Wilcoxon rank-sum test

Tests for difference between two independent variables - takes into account magnitude and direction of difference

Wilcoxon sign-rank test

Tests for difference between two related variables - takes into account magnitude and direction of difference

Sign test

Tests if two related variables are different – ignores magnitude of change, only takes into account direction

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