MCQs Estimation Quiz 8

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MCQs Estimation Quiz from Statistical Inference covers the topics of Estimation (Confidence Interval) and Bayes Factor for the preparation of exams and different statistical job tests in Government/ Semi-Government or Private Organization sectors. This test will also help get admission to different colleges and Universities. The online MCQS Estimation quiz will help the learner understand the related concepts and enhance their knowledge.

Online MCQs Estimation Quiz with Answers

1. When a Bayesian t-test yields a $BF = 0.1$, it is ten times more likely that there is no effect than that there is an effect.

 
 
 

2. Suppose that a research article indicates a value of p = .30 in the results section ($\alpha = 0.05$). The probability that the given study’s results are replicable is not equal to $1-p$.

 
 
 

3. If two 95% confidence intervals around the means overlap, then the difference between the two estimates is necessarily non-significant ($\alpha = 0.05$).

 
 
 

4. Two researchers are investigating if people can see in the future. Person A believes there is no effect, which would mean that p-values are distributed as a —————-. B finds a test statistic at the very far end of the distribution, which means that —————-.

 
 
 
 

5. Suppose that a research article indicates a value of $p = 0.30$ in the results section ($\alpha = 0.05$). You have absolutely proven the null hypothesis (that is, you have proven that there is no difference between the population means).

 
 
 

6. An observed 95% confidence interval does not predict that 95% of the estimates from future studies will fall inside the observed interval.

 
 
 

7. Suppose that a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The value $p = 0.001$ does not directly confirm that the effect size was large.

 
 
 

8. Suppose that a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The p-value gives the probability of obtaining a significant result whenever a given experiment is replicated.

 
 
 

9. A Bayes Factor close to 1 (inconclusive evidence) means that the effect size is small.

 
 
 

10. To conclude that the difference between the two estimates is non-significant ($\alpha = 0.05$), the two 95% confidence intervals around the means do not overlap.

 
 
 

11. The probability of finding a significant result when there is no true effect is called ————– The probability of finding a significant result when there is a true effect, is called —————.

 
 
 
 

12. Suppose a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The probability that the given study’s results are replicable is not equal to $1-p$.

 
 
 

13. A Bayes Factor that provides strong evidence for the alternative model does not mean the alternative hypothesis is true.

 
 
 

14. The likelihood ratio of the two hypotheses gives information about ————–, but not about —————-.

 
 
 
 

15. Suppose, the Bayesian method is used to estimate a population mean of 10 with a 95% credible interval from 8 to 12, which means ————–. This interval depends on —————.

 
 
 
 

16. Suppose that a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The p-value of a statistical test is the probability of the observed result or a more extreme result, assuming the null hypothesis is true.

 
 
 

17. The specific 95% confidence interval observed in a study has a 95% chance of containing the true effect size.

 
 
 

18. How are the three paths to statistical inference (frequentist, likelihood, Bayesian) related to each other?

 
 
 
 

19. When a Bayesian t-test yields a $BF = 10$, it is ten times more likely that there is an effect than that there is no effect.

 
 
 

20. A Bayes Factor that provides strong evidence for the null model does not mean the null hypothesis is true.

 
 
 

MCQs Estimation Quiz with Answers

  • An observed 95% confidence interval does not predict that 95% of the estimates from future studies will fall inside the observed interval.
  • Suppose a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The probability that the given study’s results are replicable is not equal to $1-p$.
  • Suppose that a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The value $p = 0.001$ does not directly confirm that the effect size was large.
  • Suppose that a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The p-value of a statistical test is the probability of the observed result or a more extreme result, assuming the null hypothesis is true.
  • The specific 95% confidence interval observed in a study has a 95% chance of containing the true effect size.
  • A Bayes Factor close to 1 (inconclusive evidence) means that the effect size is small.
  • To conclude that the difference between the two estimates is non-significant ($\alpha = 0.05$), the two 95% confidence intervals around the means do not overlap.
  • If two 95% confidence intervals around the means overlap, then the difference between the two estimates is necessarily non-significant ($\alpha = 0.05$).
  • Suppose that a research article indicates a value of p = .30 in the results section ($\alpha = 0.05$). The probability that the given study’s results are replicable is not equal to $1-p$.
  • Suppose that a research article indicates a value of $p = 0.30$ in the results section ($\alpha = 0.05$). You have absolutely proven the null hypothesis (that is, you have proven that there is no difference between the population means).
  • How are the three paths to statistical inference (frequentist, likelihood, Bayesian) related to each other?
  • Two researchers are investigating if people can see in the future. Person A believes there is no effect, which would mean that p-values are distributed as a —————-. B finds a test statistic at the very far end of the distribution, which means that —————-.
  • The probability of finding a significant result when there is no true effect is called ————– The probability of finding a significant result when there is a true effect, is called —————.
  • The likelihood ratio of the two hypotheses gives information about ————–, but not about —————-.
  • When a Bayesian t-test yields a $BF = 10$, it is ten times more likely that there is an effect than that there is no effect.
  • A Bayes Factor that provides strong evidence for the alternative model does not mean the alternative hypothesis is true.
  • When a Bayesian t-test yields a $BF = 0.1$, it is ten times more likely that there is no effect than that there is an effect.
  • Suppose that a research article indicates a value of $p = 0.001$ in the results section ($\alpha = 0.05$). The p-value gives the probability of obtaining a significant result whenever a given experiment is replicated.
  • A Bayes Factor that provides strong evidence for the null model does not mean the null hypothesis is true.
  • Suppose, the Bayesian method is used to estimate a population mean of 10 with a 95% credible interval from 8 to 12, which means ————–. This interval depends on —————.
Online MCQs Estimation Quiz with Answers

Statistical inference is a branch of statistics in which we conclude (make some wise decisions) about the population parameter using sample information. Statistical inference can be further divided into the Estimation of the Population Parameters and the Hypothesis Testing.

Estimation is a way of finding the unknown value of the population parameter from the sample information by using an estimator (a statistical formula) to estimate the parameter. One can estimate the population parameter by using two approaches (I) Point Estimation and (ii) Interval Estimation.

R Programming Language

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