Hypothesis Testing Problems and Error Analysis
Q1. Meditation and Stress Levels
- Problem: Testing if meditation lowers stress levels.
- Background: Average stress score is 42.
- Sample Data: 87 people meditated, average score 38, standard deviation 25.
- Hypothesis Test:
- Null Hypothesis (H_0): Meditation has no effect on stress levels.
- Alternative Hypothesis (H_a): Meditation lowers stress levels.
- Significance Level: \alpha
- Sampling Distribution of H_a:
- Decision: (Reject / Fail to reject) H_0
- Conclusion:
- There (is / is not) sufficient evidence to say that meditation lowers stress levels.
- Error Analysis:
- Possible Type I or Type II error?
- Consequences of the error.
Errors in Hypothesis Testing (Question 1 Context)
- Type I Error: Rejecting the null hypothesis when it is true (false positive).
- Type II Error: Failing to reject the null hypothesis when it is false (false negative).
Q2. Honey vs. Conventional Salves for Wound Infections
- Problem: Comparing honey to conventional salves in treating wound infections.
- Background: 9\% of wounds treated with conventional salves get infected.
- Sample Data: 150 wounds treated with honey, 29 became infected.
- Hypothesis Test:
- Null Hypothesis (H_0): Honey's infection rate is the same as conventional salves (9%).
- Alternative Hypothesis (H_1): Honey's infection rate is different from conventional salves.
- Significance Level: \alpha
- Sampling Distribution of H_1:
- Decision: (Reject / Fail to reject) H_1
- Conclusion:
- There (is / is not) sufficient evidence to say that honey's infection rate is different.
Effects of Changing Parameters on Error Probabilities (Question 2 Context)
- Sample Size:
- Increasing sample size DECREASES the probability of committing a Type I Error.
- Alpha (Significance Level):
- Decreasing alpha INCREASES the probability of committing a Type II Error.
- Distance Between H_0$$ and True Population:
- Increasing distance INCREASES the probability of committing a Type I Error.