All bites
The whole library, newest first. Filter by what you are here for, or pick a topic if you already know.
4330 bites
Page 64
Which three data sources would you analyze to improve activation?
This tests whether you ground hypotheses in diverse evidence before experimenting. A strong answer names qualitative feedback, funnel metrics, and behavioral analytics as distinct inputs.
What framework decides between low-effort/low-impact and high-effort/high-impact experiments?
This tests structured experiment sequencing beyond gut instinct. A strong answer picks ICE, RICE, or PIE; scores both experiments by impact, confidence, and effort or reach; then weighs opportunity cost and bandwidth.
Explain RICE scoring and its Confidence factor
Score equals Reach times Impact times Confidence divided by Effort; Confidence discounts uncertain estimates; ground it in evidence tiers.
Develop a testable hypothesis for a 40% email verification drop-off
This tests structured hypothesis formation under uncertainty. Strong answers: segment the 40% drop by device and latency; build a Customer Theory from data; isolate one lever; draft a four-part MECLABS hypothesis. Red flag: skipping diagnosis to guess fixes.
When increasing CTA clicks, what side-effects and guardrails should you consider?
This tests balancing growth with business safety. Name guardrails like retention, revenue, fraud, load time; set NI thresholds pre-launch; and include SRM checks. A red flag is treating guardrails as optional success metrics instead of hard stop limits.
Combine qualitative and quantitative data for hypotheses
Quant reveals what and where, qual reveals why, then triangulate into a falsifiable hypothesis with a metric.
How would you structure your growth team's experimentation portfolio?
3 asset classes (iterative 30-70%, tech investments, big bets 20-40%), use expected value per week, and evolve the mix.
WAU is flat despite positive A/B tests; why and how to diagnose
This tests distinguishing real impact from statistical artifacts. Strong answers cite false positives from low base rates, peeking, novelty, and local-global mismatches. Diagnose with long-term holdouts, audits, and causal bridges.
Build an opportunity-sizing model before building
Locate the affected funnel step, estimate addressable population times a bounded conversion lift times value per user, then sanity-check against a realistic ceiling.
How do you set up a button color A/B test?
Tests whether you can define a valid experiment, not just a color change. A strong answer covers hypothesis, randomization, primary metric, sample size, and significance threshold.
What is a p-value in A/B tests and what does threshold mean?
P-value is the chance of this or more extreme result if the null is true; the 5% threshold is the accepted false-positive rate.
Explain statistical power, MDE, and sample size impact
Power is the chance of detecting a true effect; MDE is the smallest lift worth measuring; raising power or shrinking MDE inflates sample size.
When user-level A/B tests get contaminated
Network or marketplace spillover violates SUTVA, so randomize by cluster (geo, group, time) and analyze at that level.
What is the multiple comparisons problem and how to correct?
This tests your grasp of family-wise error inflation across many tests. A strong answer defines the problem, contrasts per-comparison and family-wise error, and names corrections like Bonferroni or FDR.
How would you implement a last-touch attribution model for user signups?
Tests your ability to translate marketing concepts into warehouse SQL. A strong answer covers UTM/pageview events, sessionized tables, and a windowed join for the last touch within 30 days of signup.
Architect an A/B test for paid-ad signup flows
Tests pre-auth bucketing and funnel attribution. Hash a stable anonymous ID for fast assignment; stream events via Kafka into hourly aggregates; run t-tests on signup rates. Red flag: assigning after signup starts or DB lookups per assignment.
Design a programmatic SEO system for 1 million landing pages
Tests data infrastructure thinking, not content generation. Covers one-row-one-page schema, template rendering with edge caching, hierarchical routing, and crawl-budget controls via sitemaps. Red flag: AI bulk writing without structured data or caching.
How would you instrument a 4-step onboarding wizard?
Track Step Started and Step Completed with step_index and flow_variant; tie via distinct_id.
How do you determine if a user is 'new' for a setup guide?
This tests whether you separate account age from user state for onboarding. Good answers compare created_at (brittle) with a persistent flag (idempotent) and consider milestones. A red flag is using a timestamp as a permanent new proxy without managing reruns.
How would you validate that early Project creation drives retention?
Tests causal rigor on behavioral predictors. Good answer: define D30 retention and the 24-hour treatment; pull timestamps and covariates; cohort-compare with propensity matching; show lift with confidence intervals and propose an A/B nudge.