Significance Tests (Hypothesis Testing)

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Question 1

Which of the following best describes a null hypothesis (H₀)?

  • The null hypothesis states there is an effect or a difference.

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

  • The null hypothesis is what the researcher aims to prove.

  • The null hypothesis is always true.

Question 2

What does an alternative hypothesis (H₁) suggest?

  • There is no change or effect.

  • There is a change, effect, or difference.

  • The results are inconclusive.

  • The null hypothesis is correct.

Question 3

If the significance level (alpha) is set at 0.05, what does this mean?

  • There is a 5% chance of rejecting a true null hypothesis.

  • There is a 5% chance of accepting a false null hypothesis.

  • There is a 95% chance of rejecting a false null hypothesis.

  • There is a 95% chance of accepting a true null hypothesis.

Question 4

What does a p-value of 0.03 indicate in a significance test?

  • The null hypothesis is definitely true.

  • The null hypothesis is definitely false.

  • There is a 3% probability that the observed data occurred by chance under the null hypothesis.

  • There is a 97% probability that the observed data occurred by chance under the null hypothesis.

Question 5

What is a Type I error in hypothesis testing?

  • Failing to reject a true null hypothesis.

  • Rejecting a false null hypothesis.

  • Rejecting a true null hypothesis.

  • Failing to reject a false null hypothesis.

There are 5 questions to complete.

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