Guide on Selecting the Appropriate Statistical Test

Choosing the right statistical test is crucial for analyzing your data accurately. This guide will help you navigate the process by considering the type of data you have and the research question you aim to answer.

  1. Understand Your Data

First, identify the types of variables involved in your study:

Nominal: Categories without a natural order (e.g., gender, ethnicity).

Ordinal: Categories with a natural order but no fixed intervals (e.g., pain scale ratings).

Interval: Continuous data with equal intervals but no true zero (e.g., temperature in Celsius).

Ratio: Continuous data with equal intervals and a true zero (e.g., weight, height).

  1. Determine Your Research Question

Consider what you want to find out:

Comparing groups: Are you comparing the means or medians of two or more groups?

Association between variables: Are you examining the relationship between two variables?

Predicting an outcome: Are you trying to predict the value of one variable based on another?

  1. Select the Statistical Test

Once you understand the type of data you h you can utilise the flowchart below to determine which statistical test to use. Below is the best flowchart that I have found and it is from:


  1. Consider Assumptions

Each statistical test has assumptions that must be met for the results to be valid. For example:


Normality: Many tests assume that the data are normally distributed. Use tests like the Shapiro-Wilk test to check normality.

Homogeneity of variances: Tests like ANOVA assume equal variances across groups. Use Levene’s test to check this assumption.

Independence: Data points should be independent of each other. Ensure that the design of your study meets this criterion.


  1. Use Software Tools

Statistical software (e.g., SPSS, R, SAS, Stata) can help perform these tests and check assumptions. They also provide detailed documentation and guidelines for selecting the appropriate test.

Please log in to join the chat