Statistics Professional Standards & Competencies 4 — Questions and Answers
Question 1: Which of the following is a core competency specified in the American Statistical Association's endorsed data science framework?
- Proficiency in at least three programming languages
- Ability to communicate statistical uncertainty to non-technical audiences (Correct answer)
- Mastery of machine learning without classical statistical training
- Specialization in a single domain such as medicine or finance
Correct answer: Ability to communicate statistical uncertainty to non-technical audiences
Communicating uncertainty and statistical findings clearly to diverse audiences is a foundational competency in professional statistical practice.
Question 2: When reporting a confidence interval, professional standards require that the statistician clearly communicate:
- That the true parameter has a 95% probability of falling within the interval
- The procedure used, confidence level, and what the interval estimates (Correct answer)
- That the interval will contain the sample statistic 95% of the time
- Only the width of the interval, not the procedure
Correct answer: The procedure used, confidence level, and what the interval estimates
Professional reporting requires specifying the estimation procedure, confidence level, and the quantity being estimated to avoid misinterpretation.
Question 3: A data scientist notices that a predictive model performs well overall but poorly for a minority subgroup. Professional standards suggest they should:
- Report only overall performance metrics since they are statistically more stable
- Disclose the differential performance and recommend targeted model improvements (Correct answer)
- Exclude the subgroup from the evaluation sample to improve reported accuracy
- Weight the subgroup's outcomes to match majority group performance
Correct answer: Disclose the differential performance and recommend targeted model improvements
Algorithmic fairness and professional ethics require disclosing subgroup performance disparities and actively working to address them.
Question 4: Under GDPR and U.S. statistical agency standards, which principle governs limiting data use to the purpose for which it was originally collected?
- Data minimization
- Purpose limitation (Correct answer)
- Storage limitation
- Data accuracy
Correct answer: Purpose limitation
Purpose limitation prohibits using personal data for purposes incompatible with those disclosed at the time of collection.
Question 5: A statistician conducting a meta-analysis should be most concerned about which threat to the validity of conclusions?
- Heterogeneity of effect sizes across studies
- Publication bias favoring positive results (Correct answer)
- Variation in sample sizes across included studies
- Differences in statistical software used by original authors
Correct answer: Publication bias favoring positive results
Publication bias systematically excludes null results, causing meta-analyses to overestimate true effect sizes.
Question 6: The concept of 'statistical significance' versus 'practical significance' is most relevant to which professional obligation?
- Choosing the correct hypothesis test family
- Communicating the real-world importance of findings beyond p-values (Correct answer)
- Selecting sample sizes large enough for adequate power
- Ensuring compliance with FDA reporting standards
Correct answer: Communicating the real-world importance of findings beyond p-values
Statisticians must help stakeholders understand that a statistically significant result may have negligible practical importance, especially in large samples.
Question 7: Which of the following best describes the professional standard for handling 'data dredging' (exploratory analysis of many variables)?
- It should be avoided entirely in professional statistical practice
- Results must be clearly labeled as exploratory and not presented as confirmatory (Correct answer)
- It is acceptable if corrections for multiple comparisons are applied
- It is permissible only for datasets with more than 1,000 observations
Correct answer: Results must be clearly labeled as exploratory and not presented as confirmatory
Exploratory findings must be transparently labeled as hypothesis-generating, not confirmatory, and should be validated in independent datasets.
Statistics Professional Standards & Competencies 4
Which of the following is a core competency specified in the American Statistical Association's endorsed data science framework?