Overview
This lecture covers the essentials of two population proportion hypothesis tests, focusing on test statistics, significance, and interpreting outcomes.
Test Statistics and Significance
- Understand the sentence describing your hypothesis test and be able to explain it clearly.
- A test statistic indicates whether sample data significantly disagrees with the null hypothesis.
- Significance is determined by whether the test statistic falls in the tail defined by the critical value.
- The p-value helps quantify how much the sample data disagrees with the null hypothesis.
Basics of Hypothesis Testing
- The procedure for testing hypotheses with two population proportions closely follows steps from previous hypothesis tests.
- Mastery of basics (statements, calculations, interpretations) builds a foundation for all related statistical tests.
Key Terms & Definitions
- Null Hypothesis (H₀) — The assumption that there is no effect or no difference between groups being compared.
- Test Statistic — A standardized value used to decide whether to reject the null hypothesis.
- Critical Value — The threshold that the test statistic must exceed for results to be considered significant.
- P-value — The probability of observing test results at least as extreme as those obtained, assuming the null hypothesis is true.
- Two Population Proportion Test — A hypothesis test that compares proportions from two different populations.
Action Items / Next Steps
- Review your understanding of hypothesis statements, test statistics, p-values, and critical values.
- Practice explaining hypothesis test results in plain language.
- Prepare for next class on related statistical tests.