Answer: P(Type I error) decreases | P(Type II error) increases

## Explanation Let's analyze each change separately: ### 1. Decreasing level of significance from 5% to 1% - **Type I Error (α)**: This is directly set by the significance level. Decreasing from 5% to 1% means P(Type I error) decreases from 0.05 to 0.01. - **Type II Error (β)**: When α decreases, the test becomes more conservative, making it harder to reject the null hypothesis. This typically increases the probability of Type II error. ### 2. Removing 500 state-run firms from the sample (reducing sample size) - **Type I Error (α)**: Sample size reduction generally doesn't affect the Type I error rate, as α is predetermined. - **Type II Error (β)**: Reducing sample size decreases the power of the test (1-β), which means β increases. Smaller samples provide less information, making it harder to detect true effects. **Summary:** - Decreasing significance level: P(Type I error) decreases, P(Type II error) increases - Reducing sample size: P(Type I error) remains unchanged, P(Type II error) increases Looking at the table options, option B correctly states: - Level of significance decrease: P(Type I error) decreases - Reduction in sample size: P(Type II error) increases

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