Statistics Foundation · Lesson 4.4
P-values, errors and power.
This lesson deepens the hypothesis testing framework by focusing on p-value interpretation, Type I and Type II errors, power, effect size and practical importance. Students learn how to move beyond “significant or not” toward responsible statistical interpretation.
Lesson route
Move from testing mechanics to interpretation quality.
Lesson 4.3 introduced test statistics and p-values. Lesson 4.4 asks deeper questions: What can go wrong? What does power mean? How do sample size, variability and effect size shape evidence?
0–15 min
Deep p-value interpretation
Move beyond the basic definition and understand what p-values do and do not measure.
15–35 min
Decision errors
Study Type I error, Type II error, significance level and the consequences of wrong decisions.
35–60 min
Power
Understand power as the probability of detecting an effect when a real effect exists.
60–85 min
Effect size and practical importance
Separate statistical significance from the size and real-world importance of an effect.
85–115 min
Power drivers
Explore how sample size, variability, effect size and α influence statistical power.
115–150 min
Responsible interpretation
Learn how to report p-values, uncertainty, errors, power and practical meaning together.
Mastery checklist
Students should interpret statistical evidence with caution and context.
Interpret p-values correctly.
Avoid common p-value misinterpretations.
Define Type I error and α.
Define Type II error and β.
Explain power as 1 − β.
Describe how sample size affects power.
Separate statistical significance from practical importance.
Report results with effect size and uncertainty.