What is the primary purpose of hypothesis testing?, A. To calculate population parameters exactly, B. To make decisions or inferences about a population using sample data, C. To eliminate all uncertainty in data analysis, D. To describe data visually, What does the alternative hypothesis (H₁ or Hₐ) represent?, A. A statement that the null hypothesis is always false, B. A claim of no relationship between variables, C. A statement indicating the presence of an effect or difference, D. The probability of committing a Type I error, The null hypothesis (H₀) typically states that:, A. There is a significant effect or difference, B. The researcher’s prediction is correct, C. There is no effect or no difference, D. The sample mean equals the population mean, What is a p-value?, A. The probability that the null hypothesis is true, B. The probability of observing results as extreme as the sample data, assuming H₀ is true, C. The probability of making a Type II error, D. The level of significance chosen by the researcher, If the p-value is less than or equal to the significance level (α), what should you do?, A. Fail to reject the null hypothesis, B. Accept the null hypothesis as true, C. Reject the null hypothesis, D. Increase the sample size, A commonly used significance level (α) in hypothesis testing is:, A. 0.50, B. 0.05, C. 0.10, D. 0.01, What is a Type I error?, A. Failing to reject a false null hypothesis, B. Rejecting a true null hypothesis, C. Accepting a true null hypothesis, D. Using the wrong statistical test, What is a Type II error?, A. Rejecting a true null hypothesis, B. Failing to reject a false null hypothesis, C. Choosing an incorrect significance level, D. Miscalculating the test statistic, Which of the following best describes statistical power?, A. The probability of rejecting a true null hypothesis, B. The probability of failing to reject a true null hypothesis, C. The probability of rejecting a false null hypothesis, D. The probability that results are due to chance, Which factor generally increases the power of a hypothesis test?, A. Smaller sample size, B. Higher significance level (α), C. Greater variability in data, D. Lower effect size

Hypothesys Testing

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