This module is part of the 2027 curriculum. You are following the 2026 curriculum, where it is not taught in this form. Switch if you are sitting the exam under the 2027 curriculum.
Lesson 17 of 22 · 13 min
Sample size, p-values and many correlations at once
Whether a correlation counts as significant depends heavily on how many observations you have; with enough data even a tiny correlation passes the test.
In short
- For a fixed r, more data raises the chance of rejecting in two ways: grows, and the critical value shrinks as df rise.
- So the smallest |r| that is significant falls as n grows; a larger sample gives the test more power.
- In very large datasets almost every correlation is significant, so significance alone says little about how strong or useful a relationship is.
- A p-value is the smallest significance level at which can be rejected: reject when p < .
- With k variables there are distinct correlations, and each is tested separately.
Unlock this lesson free for 7 days
Create a free account to get 7 days of full access — every lesson, video, flashcard, mock and the question bank. No card needed.