Practice P-Value in Statistics

Use these practice problems to test your method after reviewing the concept explanation and worked examples.

Quick Recap

The p-value is the probability of observing results at least as extreme as the actual data, calculated under the assumption that the null hypothesis is true. A small p-value (typically below 0.05) suggests the observed data is unlikely under the null, providing evidence against it.

P-value answers: 'If nothing special is really happening, how surprising is my data?' A tiny p-value (like 0.01) means your results would be very rare if the null were true - so maybe the null is wrong. A large p-value means your results aren't surprising under the null.

Example 1

hard
A two-tailed z-test gives z = -2.65. The p-value is approximately 0.008. If \alpha = 0.05, should we reject H_0?

Example 2

hard
A test gives a p-value of 0.12. Interpret this and state the decision at \alpha = 0.05.

Example 3

hard
A researcher obtains p = 0.03. Would the result be significant at \alpha = 0.05? At \alpha = 0.01?

Example 4

hard
A hypothesis test gives a p-value of 0.20. What decision would you make at \alpha = 0.10 and at \alpha = 0.05?
← Back to P-Value Worked Examples