| name | work-sample-tests |
| description | Use when designing work samples or job auditions for hiring — covers scope, scoring, fairness, and role-specific patterns. |
Work Sample Tests
Why It Matters
Work samples are among the most predictive selection tools (Schmidt & Hunter, 1998; r ≈ .44–.54 for general work samples, higher for highly representative samples). They evaluate the work, not the person — directly testing the candidate's ability to produce the output the job requires.
Design Principles
- Representative: mirrors actual work, not abstract puzzles
- Bounded: 60–120 min onsite; take-homes capped (3 hours common; compensated above ~3 hours)
- Same prompt for every candidate at the same level
- Multi-evaluator: 2+ reviewers with shared rubric
- Discussed, not just produced: candidate walks through their work and reasoning
Role-Specific Patterns
Software Engineer
- Coding task on a representative codebase or a clean problem with realistic constraints
- Debugging exercise on real-looking code
- Design discussion of an actual recent system decision
- Pair-programming on a real bug or feature
- Avoid: abstract algorithmic puzzles uncorrelated with the work
Designer
- Critique of existing work (yours or theirs)
- Small design task with explicit constraints
- Portfolio walkthrough with deep questioning
- Working session on a real problem
Product Manager
- Analysis of a real product decision the company made
- Hypothetical case mirroring an actual challenge
- Roadmap exercise with constraints
- Customer interview synthesis exercise
Sales / Account Executive
- Pitch a product (theirs or yours) to a panel
- Discovery call role-play with realistic objections
- Account plan for a hypothetical customer
- Forecast discipline conversation
Customer Success / Support
- Customer call role-play with escalation
- Account health analysis
- Difficult customer email drafting exercise
- Renewal conversation role-play
Marketer
- Channel strategy doc for a hypothetical
- Writing exercise (blog, email, ad copy)
- Analysis of a recent campaign with critique
- Persona / ICP exercise
Recruiter
- Role intake call role-play with hiring manager
- Sourcing exercise on LinkedIn / GitHub
- Candidate brief writing
- Difficult candidate conversation
Executive / Senior Leader
- 30-60-90 plan based on a brief
- Org design review of a real or hypothetical structure
- Customer or board presentation
- Difficult conversation role-play
- References-based deep-dive
Scoring
Pre-defined rubric per work sample with:
- 3–5 evaluation dimensions (e.g., quality, judgment, communication, technical skill)
- Anchored ratings per dimension (what does 4/4 look like?)
- Multiple evaluators independently score before debrief
- Specific evidence cited
Fairness Considerations
- Time bounds matter for equity: long take-homes disadvantage candidates with caregiving responsibilities, second jobs, less flexibility
- Compensate substantial take-homes: signals respect; reduces self-selection bias
- Don't assume universal context: don't ask "redesign Stripe's checkout" if the candidate hasn't used Stripe
- Accommodations: candidates with disabilities may need additional time or different format
- Distinguish skill from style: a less polished output that demonstrates strong reasoning may be better than a polished one with shallow thinking
Common Failures
- Work samples that don't represent the job (algorithmic puzzles for product engineering)
- Take-homes that grow to 10+ hours
- No standardization across candidates
- Single evaluator scoring
- Work samples evaluated by people who can't actually judge the work
- Using the work product (the candidate gives away IP)
Take-Home vs. Onsite
| Option | Pros | Cons |
|---|
| Onsite (60–120 min) | Time-bounded; consistent conditions; less candidate burden | Pressure may not reflect real work conditions |
| Take-home (capped) | Reflects realistic work conditions; allows depth | Time disparity across candidates; risk of unpaid labor |
| Pair / live coding | Reveals reasoning and collaboration | Performance under observation may not generalize |
Most loops benefit from a combination.
Key References
- Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology.
- Bock, L. (2015). Work Rules!
- Industry practice from technical hiring guides (e.g., interview frameworks at Stripe, GitLab, Atlassian)
