Overview
The lecture discusses how to set up a hypothesis test comparing the rates of death or disability in premature infants given caffeine therapy versus a placebo, focusing on two population proportions.
Identifying Groups in the Study
- There are two groups: infants given caffeine therapy (treatment group) and infants given a placebo (control group).
- Each group's outcome is whether infants suffered from death or disability (categorical variable: yes/no).
Recognizing the Type of Study
- This is a controlled experiment with a treatment and a control group.
- The main research question is whether caffeine therapy lowers the rate of death or disability compared to placebo.
Setting Up the Hypothesis Test
- Hypothesis testing is suitable because the question compares two populations using the word "lower."
- The letter P is used in hypotheses because the outcome is categorical (proportion of infants affected).
- This is a test of two population proportions (P1 for caffeine group, P2 for placebo group).
Writing Hypotheses
- The null hypothesis (H0): P1 = P2 (proportion affected is the same in both groups).
- The alternative hypothesis (Ha): P1 < P2 (proportion affected is lower in caffeine group due to the word "lower").
Key Terms & Definitions
- Treatment group — infants who receive caffeine therapy.
- Control group — infants who receive a placebo.
- Categorical variable — a variable with categories, e.g., suffered or did not suffer death/disability.
- Population proportion (P) — the proportion of a group with a certain characteristic, such as death or disability.
- Null hypothesis (H0) — the default assumption that proportions are equal.
- Alternative hypothesis (Ha) — the assumption being tested; that the treatment changes the outcome.
Action Items / Next Steps
- Practice setting up hypothesis tests for two proportions using similar study scenarios.