DSRT 734: Interpreting a P-Value of 0.051

DSRT 734 Question

If you perform a test and get a p-value = 0.051 you should fail to
reject the null hypothesis.

True

False

What this question assesses

This item assesses whether a statistical decision is made by comparing a p-value with a prespecified significance level and whether the conclusion uses “reject” or “fail to reject” rather than claiming the null hypothesis is proven.

How to approach this type of question

Begin by identifying the significance level established before the test. A p-value has no universal pass/fail interpretation without that threshold and the analysis plan. Place the given p-value and the chosen alpha on the same scale, then apply the course’s decision rule consistently. Keep the statistical decision separate from an interpretation of practical importance, evidence quality, and study design. Also distinguish “fail to reject” from “accept”: the former means the evidence did not cross the selected threshold under the model, not that the null statement has been demonstrated as true. Borderline values should not be rounded in a way that changes the decision, and they should not be described as proof of either no effect or a meaningful effect. Work through those checks and then evaluate the statement. This guidance intentionally does not mark True or False.

For a robust response, write the assumed alpha beside 0.051 without rounding either value, apply the inequality rule, and state the statistical decision in precise language. Then add a separate note about uncertainty: a value near a conventional boundary does not create a fundamentally different underlying effect from a nearby value on the other side. The item tests the formal rule, while sound interpretation still considers design quality, power, effect size, and confidence intervals.

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Related DSRT 734 resources

• University of the Cumberlands course resources
• DSRT 734 Inferential Statistics in Decision-Making
• DSRT 734 Questions: Inferential Statistics in Decision-Making
• Interpreting P-Values, Confidence Intervals, and Effect Sizes