Issues in Significance Tests MCQs
Issues in Significance Tests MCQs
Welcome to the Issues in Significance Tests MCQs page on MCQss.com. This page is dedicated to testing your knowledge and understanding of the various issues that arise in significance tests.
Significance tests are commonly used in statistical analysis to determine the statistical significance of research findings. However, there are several important issues and considerations that researchers need to be aware of when conducting significance tests.
By engaging with the Issues in Significance Tests MCQs, you will explore and evaluate your understanding of these critical issues. The MCQs cover topics such as Type I and Type II errors, power analysis, sample size determination, effect size, confidence intervals, and interpreting p-values.
Understanding the issues in significance tests is crucial for researchers to ensure the reliability and validity of their findings. These issues affect the accuracy of statistical inferences and can have significant implications for research conclusions and decision-making.
Through regular practice of the Issues in Significance Tests MCQs, you will enhance your knowledge and proficiency in addressing these issues effectively. You will gain insights into the concept of Type I and Type II errors, learn how to calculate statistical power, determine appropriate sample sizes, assess effect sizes, and make informed decisions based on significance test results.
By mastering the complexities of significance tests, you will be equipped to conduct rigorous and reliable statistical analyses. You will have a deeper understanding of the challenges and limitations associated with significance testing and be better prepared to address these issues in your research endeavors.
Take advantage of the Issues in Significance Tests MCQs available on MCQss.com to test your understanding, refine your knowledge of these critical issues, and enhance your skills in conducting robust significance tests. These MCQs will not only assess your proficiency but also provide valuable insights into the complexities of statistical inference and the importance of addressing issues in significance testing.
1: Cohen’s d is a unit-free effect size that is not related to N, sample size.
A. True
B. False
2: Statistical significance is _______ by looking at a p value associated with a test statistic .
A. Evaluated
B. Control
C. Maintain
D. None of these
3: Clinical or Practical Significance is a difference between M and μhyp, or between two or more sample means, that is ________ .
A. Large enough
B. Small enough
C. Both a & b
D. None of these
4: Post Hoc Power Analysis can use an effect size from a completed study to evaluate statistical power for a _______ , of course.
A. Future planned study
B. Present planned study
C. Past planned study
D. None of these
5: A decision to reject H0 when H0 is correct ,this statement is related to _______ .
A. Type Iv Error
B. Type I Error
C. Type II Error
D. Type vI Error
6: A decision not to reject H0 when H0 is incorrect is known as ______ .
A. Type Iv Error
B. Type I Error
C. Type II Error
D. Type vI Error
7: A study is underpowered if the sample size is too small to have a reasonable chance of rejecting H0 when H0 is false is known as ______ .
A. Underpowered.
B. Confirmatory Study
C. Exploratory Studies
D. None of these
8: A study that includes a small number of a priori hypotheses and variables and a small number of statistical significance tests is known as _______.
A. Confirmatory Study
B. Exploratory Studies
C. Bivariate Outlier
D. None of these
9: Confirmatory Study includes a small number of a priori and a _______ small number of statistical significance tests.
A. Hypotheses
B. Variables
C. Both a & b
D. None of these
10: Exploratory Studies include ________ of variables and may evaluate large numbers of hypotheses .
A. Large numbers
B. Small number
C. Medium number
D. None of these
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Issues in Significance Tests MCQs
