ChaveryResearch Companion

Independent t-test

Do two separate groups differ on average?

When should I use Independent t-test?

You are comparing a numerical measurement between two separate groups of participants, which is what an independent samples t-test is designed for.

What your data needs to look like

Two sets of measurements.

What Independent t-test assumes

Chavery checks each of these against your data before it runs the test, and says whether it held. It does not ask you to assert them.

Normality (Shapiro-Wilk)
The values are spread roughly symmetrically around their average, rather than bunched at one end or dominated by a few extreme readings. When this one fails, Chavery runs Mann-Whitney U instead.
Equal variance (Levene's test)
The groups being compared are about equally spread out. A group that varies far more than the others makes the comparison of their averages unreliable. When this one fails, Chavery runs Welch's t-test instead.

What Chavery offers instead, and when

What Chavery does with it

It checks the assumptions first and says whether each one held. When an assumption fails it names the alternative test rather than running this one anyway.

Then it reports the statistic, the p-value, a confidence interval and an effect size, and writes the sentence you would put in your results chapter. The same numbers always give the same answer; nothing is generated by a language model.

Reporting it

A p-value on its own is not a result. Chavery reports the effect size beside it, because a difference can be statistically significant and too small to matter, and an examiner will ask which yours is. More on effect size.

Not sure this is the test you need? Work through the question Chavery asks and it will name the test your design calls for.

Related tests

Run it on your own data. Paste your numbers and Chavery runs Independent t-test with the assumption checks and the reported sentence — no project needed. Open the analysis tool, or let Chavery choose the test for you.