Two-Sample t-Tests – Independent and Paired Designs

A comprehensive review of key concepts related to two-sample t-tests, including their uses, assumptions, and methods of analysis.

What is the purpose of an independent samples t-test?

To compare the means of two separate groups of people on one quantitative outcome.

What are the assumptions of an independent-samples t-test?

Quantitative DV, IV between-subjects, random sampling, approximately normal distribution, homogeneity of variance.

When do you use a paired samples t-test?

When the same participants provide two related measurements or are organised into matched pairs.

What is a dependent variable (DV)?

The outcome that is measured in an experiment, which is expected to change due to the independent variable.

What does a two-sample t-test compare?

It compares the means of two groups to determine if they are statistically different from each other.

What does the observed t-value indicate in a two-sample t-test?

The calculated value that determines whether to reject the null hypothesis based on the t-distribution.

What is meant by 'degrees of freedom' in a t-test?

The number of independent values that can vary in an analysis without breaking any constraints.

What does a null hypothesis state?

That there is no effect or difference, suggesting that any observed difference is due to sampling error.

What is the significance level (α)?

The threshold probability for rejecting the null hypothesis, commonly set at 0.05.

What would indicate you can reject the null hypothesis in a t-test?

If the observed t-value is greater than the critical value based on the significance level.

How should p-values be interpreted?

P-values indicate the probability of obtaining test results at least as extreme as those observed, under the assumption that the null hypothesis is true.

What does it mean if a result is statistically significant?

It means that the results are unlikely to have occurred by chance alone, suggesting a true effect exists.