Evaluating Statistical Statements
Let's analyze each statement to determine its correctness:
- Statement A: The probability of making a Type II error is commonly denoted by $\beta$. The probability of making a Type I error (rejecting a true null hypothesis) is denoted by $\alpha$. Therefore, statement A is incorrect.
- Statement B: Effect size quantifies the magnitude of a phenomenon or the difference between groups. It indicates the strength of the observed effect, not the degree to which the null hypothesis is accepted. Accepting the null hypothesis is related to statistical non-significance, not the effect size itself. Thus, statement B is incorrect.
- Statement C: Jacob Cohen made significant contributions to statistics in psychology, particularly regarding statistical power, effect size, and the determination of sample sizes. He highlighted the importance of these concepts in research design. Therefore, statement C is correct.
- Statement D: A significant statistical test suggests that the observed result is unlikely to be due to random chance or error. It indicates that the result is statistically meaningful. Hence, statement D is correct.
- Statement E: A one-tail test is used when a researcher predicts a specific direction (positive or negative) for the outcome. It tests if the result is significantly greater than or significantly less than a value, but not both simultaneously. Therefore, statement E is correct.
Conclusion on Correct Statements
Statements C, D, and E are correct based on standard statistical principles.
Identifying the Correct Option
The option that includes only the correct statements (C, D, and E) is Option 4.