Why this chapter matters
Hypothesis tests provide a structured way to judge whether sample evidence is strong enough to challenge a baseline claim.
What you will learn
- Write valid null and alternative hypotheses for one-parameter questions.
- Compute and interpret p-values in one-sample z or t tests.
- Distinguish statistical significance from practical importance.
Lessons in this chapter
- Hypothesis testing frameworkFollow a full test workflow from claim to decision.
- One-sample tests for meansRun and interpret one-sample t tests with assumptions.
- One-sample tests for proportionsRun and interpret one-proportion z tests.
- Reading p-values and errorsExplain Type I and Type II errors in context. Read the full guide →
Study task
Test whether an online course completion rate differs from 70% using a one-sample proportion test, then explain the conclusion in plain language.
Chapter checkpoint
A test returns p = 0.03 at alpha = 0.05. What is the decision?
Reject the null hypothesis because the p-value is below the significance level.