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Research Methodology Selection and Tradeoffs Questions

Covers how to choose, justify, and execute research and analysis methods given research questions, stakeholder needs, and real world constraints such as limited time, budget, or access to users. Candidates should be able to compare qualitative methods such as interviews, usability testing, ethnography, and diary studies with quantitative methods such as surveys, analytics, split testing, and controlled experiments, and explain when and how to combine them into mixed methods designs. The topic includes core decision criteria and trade offs including generative versus evaluative goals, depth versus breadth, speed versus rigor, sample size and power considerations, cost versus validity, internal validity versus external generalizability, and short term versus longitudinal designs. Practical skills include aligning methodology to success metrics and business objectives, scoping minimal viable research designs, selecting sampling strategies and proxies, recruitment and instrumentation choices, pilot testing, estimation of sample size for quantitative work, mitigation of bias and threats to validity, documenting limitations and uncertainty, communicating and defending methodological choices to nonresearch stakeholders, and ensuring ethical and privacy safeguards and data quality in constrained or iterative studies.

HardTechnical
38 practiced
You are mentoring a junior researcher who ran an underpowered randomized controlled trial and now claims multiple significant subgroup effects. How would you coach them to critically reassess those claims, run appropriate re-analyses, design follow-up studies, and write transparent limitations for an academic submission?
EasyTechnical
38 practiced
Compare qualitative and quantitative research methods for user-facing ML features. Describe three strengths and three weaknesses of each in the context of: speed-to-insight, depth of understanding, and ability to generalize to the product population. Give one concrete example of when to prioritize each method.
EasyTechnical
34 practiced
Explain the difference between generative and evaluative research in the context of designing and evaluating an ML-powered user-facing feature (for example, an intelligent search assistant). For each type, give one concrete method (e.g., ethnography or A/B testing), describe the primary goal, and state when you would choose one over the other given constraints such as 2-week timeline, limited budget, or high regulatory risk.
MediumTechnical
43 practiced
You have limited logging access in production. Propose an instrumentation and lightweight validation plan to measure the explainability of model outputs to end users. Include proposed quantitative metrics, short survey items, and a simple in-product experiment to validate whether changes to explanations change user behavior.
HardTechnical
35 practiced
Propose a principled approach to choose stopping rules and sequential analyses for a 6-month longitudinal study with monthly checkpoints. Balance early learning (interim analyses) against inflated type I error and attrition. Include simulation-based planning, pre-specified thresholds, and how to adjust sample-size calculations for interim looks.

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