Important MCQs Hypothesis Testing Quiz 3

The post is about MCQs Hypothesis Testing Quiz. There are 20 multiple-choice questions. The quiz covers the topics related to the basics of hypothesis testing, level of significance, test statistical, critical region, parametric, and non-parametric tests. Let us start with MCQs Hypothesis Testing Quiz.

Online MCQs about Hypothesis Testing with Answers

1. If a Chi-square goodness of fit test has 6 categories and an $N=30$, then the correct number of degrees of freedom is:

 
 
 
 

2. In testing the statistical hypothesis, which of the following statement(s) is false?

 
 
 
 
 

3. In a one-sample hypothesis test of the mean, what are the typical options for the alternative hypothesis?

 
 
 
 

4. For testing the equality of several variances the appropriate test is

 
 
 
 

5. The following are percentages of fat found in 5 samples of each of the two brands of baby food:
A:    5.7, 4.5, 6.2, 6.3, 7.3
B:    6.3, 5.7, 5.9, 6.4, 5.1
Which of the following procedures is appropriate to test the hypothesis of equal average fat content in the two types of ice cream?

 
 
 
 
 

6. In statistical testing of the hypothesis, what happens to the region of rejection when the level of significance $\alpha$ is reduced?

 
 
 
 
 

7. You conduct a hypothesis test, you need to conclude and commit a type II error. Which of the following statements accurately describes this scenario?

 
 
 
 

8. In a statistical hypothesis test of equality of means, such as $H_0:\mu=10$, if $\alpha=5\%$

 
 
 
 
 

9. Which of the following statements is correct

 
 
 
 
 

10. In hypothesis testing, type II error is represented by $\beta$ and the power of the test is $1-\beta$ then

 
 
 
 
 

11. To perform a run test for randomness the data must be

 
 
 
 

12. Suppose you conduct a hypothesis test and choose a significance level of 5%. You calculate a p-value of 3.3%. What conclusion should be drawn?

 
 
 
 

13. Since   $\alpha$= probability of Type I error, then $1 -\alpha$

 
 
 
 
 

14. A data analyst conducts a hypothesis test. They fail to reject the null hypothesis. What statement best describes their conclusion?

 
 
 
 

15. The average growth of a certain variety of pine trees is 10.1 inches in three years. A biologist claims that a new variety will have greater three-year growth. A random sample of 25 of the new variety has an average three-year growth of 10.8 inches and a standard deviation of 2.1 inches. The appropriate null and alternative hypotheses to test the biologist’s claim are:

 
 
 
 
 

16. In hypothesis testing, you need to conclude, and you fail to reject a null hypothesis, which is actually false. What type of error do they commit?

 
 
 
 

17. Which of the following is NOT correct?

 
 
 
 
 

18. In testing the difference between two populations, it is possible to use

 
 
 
 

19. In testing the statistical hypothesis, which of the following statement(s) is false?

 
 
 
 
 

20. In testing of hypothesis, type-II error may be defined as:

 
 
 
 

MCQs Hypothesis Testing Quiz with Answers

MCQs Hypothesis Testing Quiz
  • In hypothesis testing, type II error is represented by $\beta$ and the power of the test is $1-\beta$ then
  • In statistical testing of the hypothesis, what happens to the region of rejection when the level of significance $\alpha$ is reduced?
  • Which of the following is NOT correct?
  • In testing the statistical hypothesis, which of the following statement(s) is false?
  • In testing the statistical hypothesis, which of the following statement(s) is false?
  • In a statistical hypothesis test of equality of means, such as $H_0:\mu=10$, if $\alpha=5\%$
  • Which of the following statements is correct
  • The average growth of a certain variety of pine trees is 10.1 inches in three years. A biologist claims that a new variety will have greater three-year growth. A random sample of 25 of the new variety has an average three-year growth of 10.8 inches and a standard deviation of 2.1 inches. The appropriate null and alternative hypotheses to test the biologist’s claim are:
  • Since   $\alpha$= probability of Type I error, then $1 -\alpha$
  • The following are percentages of fat found in 5 samples of each of the two brands of baby food: A: 5.7, 4.5, 6.2, 6.3, 7.3 B:    6.3, 5.7, 5.9, 6.4, 5.1 Which of the following procedures is appropriate to test the hypothesis of equal average fat content in the two types of ice cream?
  • In hypothesis testing, you need to conclude, and you fail to reject a null hypothesis, which is actually false. What type of error do they commit?
  • You conduct a hypothesis test, you need to conclude and commit a type II error. Which of the following statements accurately describes this scenario?
  • Suppose you conduct a hypothesis test and choose a significance level of 5%. You calculate a p-value of 3.3%. What conclusion should be drawn?
  • In a one-sample hypothesis test of the mean, what are the typical options for the alternative hypothesis?
  • A data analyst conducts a hypothesis test. They fail to reject the null hypothesis. What statement best describes their conclusion?
  • In testing the difference between two populations, it is possible to use
  • In testing of hypothesis, type-II error may be defined as:
  • If a Chi-square goodness of fit test has 6 categories and an $N=30$, then the correct number of degrees of freedom is:
  • For testing the equality of several variances the appropriate test is
  • To perform a run test for randomness the data must be
MCQS Hypothesis Testing Quiz with Answers

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2 thoughts on “Important MCQs Hypothesis Testing Quiz 3”

  1. Sir in statistical inference exercise 5 :Q11 sir how power of the test related to type 1&2 error both. Sir kindly guaid me in this regard.

    Reply
    • Thank you for visiting the site and asking questions.

      Type I and Type II errors are inversely related. A researcher wants both Type-I and Type-II errors to be small. In terms of significance level and power, Weiss says this means we want a small significance level (close to 0) and a large power (close to 1).
      Following are multiple ways of defining the power of test:

      • Power is the probability of rejecting the null hypothesis when, in fact, it is false.
      • Power is the probability of making a correct decision when the null hypothesis is false.
      • Power is the probability that a test of significance will pick up on an effect that is present.
      • Power is the probability that a test of significance will detect a deviation from the null hypothesis, should such a deviation exist.
      • Power is the probability of avoiding a Type II error.
      Reply

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