Experimentation and Product Validation Questions
Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.
EasyTechnical
0 practiced
Explain Type I and Type II errors in the context of product experiments. Describe how alpha and beta relate to these errors, give a concrete business example of each error (what bad decisions they could cause), and explain how increasing sample size affects both error types.
HardTechnical
0 practiced
Describe methods to estimate heterogeneous treatment effects (CATE) across user segments for an A/B test. Cover pre-specified vs post-hoc segmentation, interaction terms in regression, uplift models, and the multiple-testing concerns when searching many segments. For each method, mention how a BI analyst would validate and present the findings.
MediumTechnical
0 practiced
Given table assignments(user_id, bucket, assigned_at) where bucket is 'control' or 'treatment', write a Postgres SQL query that computes observed counts per bucket, expected counts under equal split, and the chi-square statistic for SRM detection. Return observed_control, observed_treatment, expected_control, expected_treatment, chi_square_stat. Describe briefly how you'd obtain a p-value if Postgres lacks a chi-square CDF function.
MediumTechnical
0 practiced
Compare frequentist and Bayesian approaches to A/B testing from a BI analyst's perspective. Discuss differences in interpretation (confidence interval vs credible interval), how sequential analysis and stopping rules differ, the operational implications for reporting to stakeholders, and practical cases where one approach may be preferred over the other.
HardTechnical
0 practiced
You need to evaluate long-term retention impact of a product change. Explain how you would use survival analysis (for example, Kaplan-Meier curves and Cox proportional hazards models) to compare treatment and control. Describe how to handle censoring, align cohorts by assignment date, check assumptions, and how you would present results to non-technical stakeholders.
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