A very complete and up-to-date tutorial on running and interpreting t-tests in SPSS. Includes:

• Assumptions
• Understanding the SPSS Output
• Effect Size (Cohen’s D)
• APA Style Reporting

An independent samples t-test examines if 2 populations have equal means on some variable.

Example: do Dutch women have the same mean salary as Dutch men?

This tutorial quickly walks you through the basics such as the assumptions, null hypothesis and effect size for this test.

A paired samples t-test examines if 2 variables have equal means in some population.

Example: were the mean salaries over 2018 and 2019 equal for all Dutch citizens?

This tutorial quickly walks you through the correct steps for running this test in SPSS.

A one-sample t-test examines if a population mean is likely to be x: some hypothesized value.

Example: do the pupils from my school have a mean IQ score of 100?

This tutorial quickly walks you through the basics for this test, including assumptions, formulas and effect size.

How to run a one sample t-test correctly in SPSS?

This simple tutorial with downloadable practice data quickly walks you through the right steps!

Cohen’s D is the effect size measure of choice for t-tests.

This simple tutorial quickly walks you through

• rules of thumb for small, medium and large effects;
• formulas for computing Cohen’s D and;
• software options for obtaining it.

Z-scores are scores that have mean = 0 and standard deviation = 1.

All scores can be standardized into z-scores by subtracting the mean from each score and then dividing it by the standard deviation.

Such standardized scores may be easier to interpret than the original scores. Z-scores may or may not be normally distributed.

Dichotomous variables are variables that hold precisely two distinct values.

Example: sex can only be male or female.

Some analyses that are only suitable for dichotomous variables are

• the independent samples t-test;
• logistic regression;
• the binomial test;
• z-tests.