What is the null hypothesis in hypothesis testing?

• The null hypothesis states that there is no effect or no difference.

• The null hypothesis suggests that the alternative hypothesis is true.

• The null hypothesis claims that there is a significant effect.

• The null hypothesis states that results are always random.

What is the alternative hypothesis in hypothesis testing?

• The alternative hypothesis is always true in hypothesis testing.

• The alternative hypothesis is a guess about the population parameter.

• The alternative hypothesis is a statement of no effect or difference.

• The alternative hypothesis is a statement indicating the presence of an effect or difference.

Define a type I error in the context of hypothesis testing.

• A type I error is the incorrect rejection of a true null hypothesis.

• A type I error is the correct rejection of a false null hypothesis.

• A type I error is the acceptance of a true null hypothesis.

• A type I error is the failure to reject a false null hypothesis.

Define a type II error in the context of hypothesis testing.

• A type II error is the failure to reject a false null hypothesis.

• A type II error is the failure to accept a true alternative hypothesis.

• A type II error occurs when the null hypothesis is accepted incorrectly.

• A type II error is the rejection of a true null hypothesis.

What does the level of significance represent in hypothesis testing?

• The level of significance represents the probability of making a Type I error in hypothesis testing.

• The level of significance indicates the sample size needed for testing.

• The level of significance is the probability of making a Type II error.

• The level of significance determines the confidence interval for the results.

How is the level of significance typically denoted?

• β (beta)

• α (alpha)

• δ (delta)

• γ (gamma)

What is the consequence of a type I error?

• The consequence of a type I error is the incorrect conclusion that a treatment or effect is effective when it is not.

• The consequence of a type I error is correctly identifying a treatment as ineffective.

• The consequence of a type I error is concluding that a treatment is harmful when it is not.

• The consequence of a type I error is failing to detect an effect that is present.

What is the consequence of a type II error?

• Correctly identifying a true effect

• Identifying a false effect or difference

• The consequence of a type II error is failing to identify a true effect or difference.

• Failing to reject a false null hypothesis

How can the level of significance affect the likelihood of type I and type II errors?

• The level of significance inversely affects Type I and Type II errors.

• Type I and Type II errors are unrelated to the level of significance.

• The level of significance has no effect on Type I errors.

• Increasing the level of significance decreases Type II errors.

Why is it important to clearly define null and alternative hypotheses?

• It guarantees accurate results regardless of the sample size.

• It simplifies the data collection process.

• It eliminates the need for statistical analysis.

• It ensures a clear framework for statistical testing and decision-making.