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156 lines
5.7 KiB
Markdown
156 lines
5.7 KiB
Markdown
---
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id: wiki-2026-0508-problem-solving-test-pst
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title: Problem Solving Test (PST)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [PST, McKinsey PST, Consulting Problem Solving Test]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [consulting, mckinsey, structured-thinking, mece, problem-solving]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: N/A
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framework: MECE / Hypothesis Tree / Issue Tree
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---
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# Problem Solving Test (PST)
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## 매 한 줄
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> **"매 McKinsey legacy paper test — 26 case-style multiple-choice questions in 60 minutes — replaced by Solve / Imbellus game in 2018-2019, but the underlying skills (structured thinking, MECE, hypothesis-tree, data interp) 매 still core to consulting interviews"**. The test is gone; the discipline 매 not.
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## 매 핵심
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### 매 history
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- **1990s-2017**: McKinsey PST = pen-and-paper screening before 1st round interview.
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- **2018-2019**: replaced by **Solve** (formerly Imbellus) — game-based assessment (ecosystem balancing, plant defense).
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- **2026**: Solve still in use; many regions also use video-interview AI screening; some clones (BCG Online Case, Bain SOVA, Deloitte Pymetrics).
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- The PST format 매 still copied by other firms / business schools.
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### 매 question types (PST format)
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1. **Word problems**: "Client X has revenue $Y, costs $Z..."
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2. **Data interpretation**: read chart/table → infer.
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3. **Logic / reading**: assumption identification, what-if.
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4. **Math**: %, ratios, breakeven, growth rates.
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### 매 core consulting frameworks (the actual skill)
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- **MECE**: Mutually Exclusive, Collectively Exhaustive — no overlap, no gap.
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- **Issue tree**: hierarchical decomposition of the problem.
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- **Hypothesis tree**: tree where each node is a falsifiable hypothesis.
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- **80/20 / Pareto**: focus on biggest drivers first.
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- **Profit tree**: Profit = (P - VC) * Q - FC; decompose each.
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- **Market sizing**: top-down (population × penetration × ARPU) vs bottom-up.
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### 매 modern Solve game
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- 6 mini-games: ecosystem, redrock study, plant defense, etc.
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- Measures: critical thinking, decision making, situational awareness, learning agility.
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- AI-graded; ~70 min total.
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## 💻 패턴
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### MECE issue tree (text representation)
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```
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Problem: "Why is profit declining?"
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├── Revenue down
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│ ├── Volume down
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│ │ ├── Fewer customers
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│ │ └── Less per customer
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│ └── Price down
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│ ├── Discounts up
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│ └── Mix shift to cheaper SKUs
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└── Costs up
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├── COGS up (input price, supplier, waste)
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└── SG&A up (headcount, marketing, IT)
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```
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### Hypothesis tree
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```
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Root hypothesis: "Profit decline is driven by margin compression in Region A."
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├── H1: input costs in A rose >5% YoY [test: supplier invoices]
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├── H2: A introduced discounting in Q3 [test: pricing data]
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└── H3: A's mix shifted to low-margin SKUs [test: SKU-level P&L]
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```
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### Profit decomposition
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```
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Profit = (Price - VariableCost) × Quantity - FixedCost
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↓ each is a lever; trace YoY delta to isolate cause
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```
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### Market sizing (top-down)
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```
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US coffee shop market size:
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≈ 330M people
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× 60% coffee drinkers
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× 250 cups/year average
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× $4 average cup
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≈ $198B (sanity-check vs $100B reported → adjust assumptions)
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```
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### Data-interp checklist (PST-style)
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1. Read title + axes + units first.
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2. What is the question actually asking?
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3. Eliminate clearly wrong options (often 2 quickly).
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4. Compute only what's needed (not all entries).
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5. Beware base-rate fallacy: % vs absolute.
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### Hypothesis-driven case interview opening
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```
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"Before diving in, I'd like to structure my thinking.
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Given the goal is [restate], I see three areas to explore:
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1. ... 2. ... 3. ... [MECE check]
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I hypothesize the answer lies in [#2] because [reason].
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I'd like to start by asking about [data needed to test #2].
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Does that approach work?"
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```
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### Quick math drills (PST training)
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```
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- Mental %: 17% of 240 → 10% (24) + 5% (12) + 2% (4.8) ≈ 40.8
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- Growth: $100 → $150 over 5 yrs → 1.5^(1/5) ≈ 1.085 ≈ 8.5% CAGR
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- Breakeven: FC / (P - VC)
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- Return on investment: ΔProfit / Investment
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Preparing for McKinsey 2026 | Practice Solve (game), not legacy PST |
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| BCG / Bain interview | Online case + structured case method |
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| Improve general problem-solving | MECE + hypothesis tree drills |
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| Ambiguous business problem | Issue tree → 80/20 → hypothesis test |
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| Quantitative case (sizing) | Top-down + bottom-up cross-check |
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**기본값**: MECE issue tree → hypothesize → data → synthesize. Format-agnostic.
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## 🔗 Graph
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- 부모: [[Problem_Solving|Problem-Solving]]
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- 변형: [[McKinsey-Solve]] · [[Case-Interview]]
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- 응용: [[Strategy-Consulting]]
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- Adjacent: [[MECE]] · [[Hypothesis Tree]] · [[Pareto-Principle]]
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## 🤖 LLM 활용
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**언제**: structured business analysis, case interview prep, strategic decomposition of ambiguous problems.
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**언제 X**: technical engineering decisions (use systems thinking instead), pure math optimization.
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## ❌ 안티패턴
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- **Boiling the ocean**: not 80/20 — analyze every branch equally.
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- **Non-MECE buckets**: overlapping or missing categories.
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- **Hypothesis without test**: "I think X" w/ no falsification plan.
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- **Data dump**: charts without "so what".
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- **Memorizing PST questions**: format is gone since 2019.
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## 🧪 검증 / 중복
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- Verified (McKinsey careers website, Minto Pyramid Principle, Case in Point textbook).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — PST history + transferable structured thinking skills |
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