Research Design and Study Planning Questions

End to end planning and design of research studies to rigorously answer product, user experience, or scientific questions. Candidates should be able to translate business or product problems into clear and testable research questions and hypotheses and convert those questions into feasible and valid study plans. Core skills include selecting appropriate qualitative, quantitative, or mixed methods, defining primary outcomes and success metrics, aligning sampling strategy and inclusion and exclusion criteria, estimating sample sizes and articulating precision and power considerations, designing recruitment approaches and consent procedures, drafting interview guides survey items and measurement instruments with attention to reliability and validity, planning data collection workflows and quality controls, and outlining statistical and qualitative analysis plans and integration strategies for mixed methods. Candidates should also be able to identify potential confounds and threats to internal and external validity and propose mitigation approaches, scope studies to remain feasible under time and resource constraints, plan logistics timelines and resource allocation, pilot and iterate instruments, address ethical and regulatory requirements such as institutional review board review and data privacy, and communicate research plans limitations and actionable findings to stakeholders. Interviewers may probe trade offs among methodologies bias mitigation strategies reproducibility and documentation practices how the candidate managed scope and stakeholder expectations and how preliminary findings or stakeholder input influenced the evolution of research questions and study scope while avoiding scope creep.

HardTechnical

57 practiced

Design an observational study to estimate the long-term causal effect of a redesigned onboarding flow on 12-month retention across three regions with staggered rollout and partial instrumentation. Propose two quasi-experimental strategies (e.g., difference-in-differences with staggered adoption, synthetic control, or instrumental variables using rollout schedule), state identification assumptions for each, list pre-analysis diagnostics (e.g., pre-trend tests, placebo checks), robustness checks, and how you'd communicate residual uncertainty to executives.

HardTechnical

40 practiced

A stakeholder asks for a combined analysis of transaction logs and marketing metadata to identify 'high-value' customers for targeted outreach, but the dataset contains quasi-identifiers that increase re-identification risk. Propose a plan that balances analytic needs and privacy: risk assessment steps, de-identification techniques, k-anonymity vs differential privacy trade-offs, data minimization practices, safe analysis environments, and documentation for compliance. How would you present the privacy-utility trade-offs to stakeholders?

EasyTechnical

49 practiced

Define a primary outcome versus secondary outcomes in a research study. Given the product objective 'increase weekly active users (WAU)', propose one primary outcome and two secondary outcomes, and explicitly describe how to operationalize each (data fields used, aggregation window, inclusion criteria, and handling of edge cases such as multiple events per user). Explain how you would reduce the risk of metric-hacking.

HardTechnical

57 practiced

Compare multi-armed bandit (MAB) adaptive experiments with fixed randomized controlled trials. For a high-traffic e-commerce homepage, discuss when you would prefer adaptive allocation (contextual bandits) over a traditional RCT, including trade-offs in regret minimization vs unbiased estimation, consequences for treatment effect estimation and heterogeneity, statistical guarantees under adaptive allocation, and operational challenges in implementation and inference.

HardTechnical

39 practiced

You will test 15 secondary outcomes and several subgroup analyses in a single experiment. Draft a principled statistical strategy to control the false positive rate while preserving power for the primary outcome. Discuss approaches including hierarchical testing (gatekeeping), familywise error vs false discovery rate (FDR) control, Bayesian hierarchical models to borrow strength, and how to pre-specify and document exploratory analyses to avoid p-hacking. Also explain how you'd communicate results to non-technical stakeholders.

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