Of the following, which is the BEST statistical practice?

Question

BlackTom AI · Accepted Answer

Interpreting results in terms of the alternative hypothesis is best because hypothesis testing logic and good inferential practice focus on estimating and assessing evidence for effect sizes rather than “proving” a null.

1. Relevant knowledge points and concepts
- Null hypothesis (H0) vs alternative hypothesis (HA): H0 typically states no effect; HA states the presence/direction of an effect.  
- P-values: quantify how surprising the observed data are if H0 were true; they do not give the probability that H0 is true.  
- Failure to reject H0 is not the same as proving H0 true—absence of evidence is not evidence of absence.  
- Best practice emphasizes estimation (effect sizes, confidence intervals), pre-registration, transparent handling of outliers, and avoiding data-driven multiple testing (p-hacking).

2. Step-by-step reasoning
- A p-value evaluates data under H0; if small, you reject H0 in favor of HA. If not small, you retain H0 but cannot conclude it is true—data may be underpowered or noisy.  
- Therefore interpreting results with respect to the alternative (e.g., magnitude and uncertainty of the effect) communicates what evidence supports and how strong it is.  
- Reporting effect sizes and confidence intervals makes the conclusion about the alternative concrete and informative.

3. Calculations
- No numeric calculation is required here. The logical intermediary step is: p-value → decision (reject/fail to reject) → interpret evidence in terms of HA and effect estimates rather than claiming null truth.

4. Why the correct answer is correct
- The chosen option correctly reflects statistical theory: tests provide evidence for HA or fail to reject H0; they never prove H0. Emphasizing interpretation in terms of HA and effect estimates leads to clearer, more honest scientific claims.

5. Why other options are incorrect
- Option 1 (run every test possible): Encourages p-hacking and inflates false positives; poor practice.  
- Option 3 (pre-register how to define high-influence points so you can exclude them): Pre-specifying criteria is good, but wording suggests excluding points post hoc; best practice is pre-specify inclusion/exclusion and report sensitivity analyses rather than simply excluding when “necessary.”  
- Option 4 (avoid figures): Figures are crucial for understanding data patterns and communicating results; they complement tests, not replace them.

6. Conclusion
The best practice is to interpret results in terms of the alternative hypothesis (focusing on effect sizes and uncertainty) rather than claiming the null is true, which confirms the provided correct answer.

Of the following, which is the BEST statistical practice?Single choice

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