Answer: All else being equal, the decrease in the chance of making a Type I error comes at the cost of increasing the probability of making a Type II error.

## Explanation Let's analyze each option: **Option A**: "Type II error refers to the failure to reject the H₁ when it is actually false." - This is **incorrect**. Type II error is the failure to reject the null hypothesis (H₀) when it is actually false, not H₁. **Option B**: "Hypothesis testing is used to make inferences about the parameters of a given population on the basis of statistics computed for a sample that is drawn from another population." - This is **incorrect**. Hypothesis testing uses a sample drawn from the same population, not from another population. **Option C**: "All else being equal, the decrease in the chance of making a Type I error comes at the cost of increasing the probability of making a Type II error." - This is **correct**. There is a trade-off between Type I and Type II errors. When we decrease the significance level (α) to reduce Type I errors, we increase the probability of Type II errors (β). **Option D**: "If the p-value is greater than the significance level, then the statistics falls into the reject intervals." - This is **incorrect**. When the p-value is greater than the significance level, we fail to reject the null hypothesis, meaning the test statistic does NOT fall into the rejection region. **Key Concepts:** - **Type I Error (α)**: Rejecting H₀ when it is actually true - **Type II Error (β)**: Failing to reject H₀ when it is actually false - **Trade-off**: Decreasing α increases β, and vice versa - **p-value interpretation**: p-value < α → reject H₀; p-value ≥ α → fail to reject H₀

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