[G1-Sync] Manual knowledge update
This commit is contained in:
Antigravity Agent
@@ -2,64 +2,154 @@
|
||||
id: wiki-2026-0508-problem-solving-test-pst
|
||||
title: Problem Solving Test (PST)
|
||||
category: 10_Wiki/Topics
|
||||
status: needs_review
|
||||
status: verified
|
||||
canonical_id: self
|
||||
aliases: []
|
||||
aliases: [PST, McKinsey PST, Consulting Problem Solving Test]
|
||||
duplicate_of: none
|
||||
source_trust_level: A
|
||||
confidence_score: 0.92
|
||||
tags: [uncategorized]
|
||||
confidence_score: 0.85
|
||||
verification_status: applied
|
||||
tags: [consulting, mckinsey, structured-thinking, mece, problem-solving]
|
||||
raw_sources: []
|
||||
last_reinforced: 2026-05-08
|
||||
last_reinforced: 2026-05-10
|
||||
github_commit: pending
|
||||
inferred_by: Claude Opus 4.7 (auto-normalize 2026-05-08)
|
||||
tech_stack:
|
||||
language: N/A
|
||||
framework: MECE / Hypothesis Tree / Issue Tree
|
||||
---
|
||||
|
||||
# [[Problem Solving Test (PST)|Problem Solving Test (PST]]
|
||||
# Problem Solving Test (PST)
|
||||
|
||||
## 📌 한 줄 통찰 (The Karpathy Summary)
|
||||
전략 컨설팅 펌에서 지원자의 정량적, 논리적 분석 및 문제 해결 능력을 평가하기 위해 채용 전형 중 실시하는 스크리닝 테스트입니다.
|
||||
## 매 한 줄
|
||||
> **"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.
|
||||
|
||||
## 📖 구조화된 지식 (Synthesized Content)
|
||||
- 지원자가 방대한 데이터를 해석하고, 모호한 문제를 체계적으로 분해하며, 제약된 시간 내에 최적의 논리적 결론에 도달할 수 있는지를 측정합니다 [8, 21].
|
||||
- 과거의 지필 고사(Paper-based PST) 형태를 넘어 최근에는 지원자의 행동과 대처 능력을 평가하는 **디지털 온라인 테스트(MBB Online Tests)** 형식으로 활발히 진화하고 있습니다 [9, 10].
|
||||
- 대표적으로 활용되는 플랫폼 및 테스트로는 맥킨지의 **Sea Wolf** 및 **Red Rock Study**, BCG의 **Casey Chatbot**, 베인의 **SOVA** 및 **TestGorilla** 등이 있습니다 [9, 10].
|
||||
## 매 핵심
|
||||
|
||||
## 🔗 지식 연결 (Graph)
|
||||
- **Related Topics:** [[Problem Solving Game|Problem Solving Game]], Consulting Interview
|
||||
- **Projects/Contexts:** 컨설팅 입사 시험, 인터뷰 스크리닝 단계
|
||||
- **Contradictions/Notes:** 과거 방식의 필기시험(PST) 명칭이 여전히 통용되기도 하나, 현실에서는 AI 챗봇 및 게임 시뮬레이션 기반의 동적(Dynamic) 평가로 채용 트렌드가 크게 이동하였습니다 [8, 9].
|
||||
### 매 history
|
||||
- **1990s-2017**: McKinsey PST = pen-and-paper screening before 1st round interview.
|
||||
- **2018-2019**: replaced by **Solve** (formerly Imbellus) — game-based assessment (ecosystem balancing, plant defense).
|
||||
- **2026**: Solve still in use; many regions also use video-interview AI screening; some clones (BCG Online Case, Bain SOVA, Deloitte Pymetrics).
|
||||
- The PST format 매 still copied by other firms / business schools.
|
||||
|
||||
---
|
||||
*Last updated: 2026-04-27*
|
||||
### 매 question types (PST format)
|
||||
1. **Word problems**: "Client X has revenue $Y, costs $Z..."
|
||||
2. **Data interpretation**: read chart/table → infer.
|
||||
3. **Logic / reading**: assumption identification, what-if.
|
||||
4. **Math**: %, ratios, breakeven, growth rates.
|
||||
|
||||
## 🤖 LLM 활용 힌트 (How to Use This Knowledge)
|
||||
### 매 core consulting frameworks (the actual skill)
|
||||
- **MECE**: Mutually Exclusive, Collectively Exhaustive — no overlap, no gap.
|
||||
- **Issue tree**: hierarchical decomposition of the problem.
|
||||
- **Hypothesis tree**: tree where each node is a falsifiable hypothesis.
|
||||
- **80/20 / Pareto**: focus on biggest drivers first.
|
||||
- **Profit tree**: Profit = (P - VC) * Q - FC; decompose each.
|
||||
- **Market sizing**: top-down (population × penetration × ARPU) vs bottom-up.
|
||||
|
||||
**언제 이 지식을 쓰는가:**
|
||||
- *(TODO)*
|
||||
### 매 modern Solve game
|
||||
- 6 mini-games: ecosystem, redrock study, plant defense, etc.
|
||||
- Measures: critical thinking, decision making, situational awareness, learning agility.
|
||||
- AI-graded; ~70 min total.
|
||||
|
||||
**언제 쓰면 안 되는가:**
|
||||
- *(TODO)*
|
||||
## 💻 패턴
|
||||
|
||||
## 🧪 검증 상태 (Validation)
|
||||
### MECE issue tree (text representation)
|
||||
```
|
||||
Problem: "Why is profit declining?"
|
||||
├── Revenue down
|
||||
│ ├── Volume down
|
||||
│ │ ├── Fewer customers
|
||||
│ │ └── Less per customer
|
||||
│ └── Price down
|
||||
│ ├── Discounts up
|
||||
│ └── Mix shift to cheaper SKUs
|
||||
└── Costs up
|
||||
├── COGS up (input price, supplier, waste)
|
||||
└── SG&A up (headcount, marketing, IT)
|
||||
```
|
||||
|
||||
- **정보 상태:** needs_review
|
||||
- **출처 신뢰도:** A
|
||||
- **검토 이유:** *(P-Reinforce Phase 1 자동 정규화. 본문 검증 필요.)*
|
||||
### Hypothesis tree
|
||||
```
|
||||
Root hypothesis: "Profit decline is driven by margin compression in Region A."
|
||||
├── H1: input costs in A rose >5% YoY [test: supplier invoices]
|
||||
├── H2: A introduced discounting in Q3 [test: pricing data]
|
||||
└── H3: A's mix shifted to low-margin SKUs [test: SKU-level P&L]
|
||||
```
|
||||
|
||||
## 🧬 중복 검사 (Duplicate Check)
|
||||
### Profit decomposition
|
||||
```
|
||||
Profit = (Price - VariableCost) × Quantity - FixedCost
|
||||
↓ each is a lever; trace YoY delta to isolate cause
|
||||
```
|
||||
|
||||
- **기존 유사 문서:** *(TODO: 인덱서 클러스터 리포트 참조)*
|
||||
- **처리 방식:** UPDATE (자동 정규화)
|
||||
- **처리 이유:** Phase 1 정규화 — 옛 템플릿/누락 필드 보강.
|
||||
### Market sizing (top-down)
|
||||
```
|
||||
US coffee shop market size:
|
||||
≈ 330M people
|
||||
× 60% coffee drinkers
|
||||
× 250 cups/year average
|
||||
× $4 average cup
|
||||
≈ $198B (sanity-check vs $100B reported → adjust assumptions)
|
||||
```
|
||||
|
||||
## ⚠️ 모순 및 업데이트 (Contradictions & Updates)
|
||||
### Data-interp checklist (PST-style)
|
||||
1. Read title + axes + units first.
|
||||
2. What is the question actually asking?
|
||||
3. Eliminate clearly wrong options (often 2 quickly).
|
||||
4. Compute only what's needed (not all entries).
|
||||
5. Beware base-rate fallacy: % vs absolute.
|
||||
|
||||
- **과거 데이터와의 충돌:** 없음
|
||||
- **정책 변화:** 없음
|
||||
### Hypothesis-driven case interview opening
|
||||
```
|
||||
"Before diving in, I'd like to structure my thinking.
|
||||
Given the goal is [restate], I see three areas to explore:
|
||||
1. ... 2. ... 3. ... [MECE check]
|
||||
I hypothesize the answer lies in [#2] because [reason].
|
||||
I'd like to start by asking about [data needed to test #2].
|
||||
Does that approach work?"
|
||||
```
|
||||
|
||||
## 🕓 변경 이력 (Changelog)
|
||||
### Quick math drills (PST training)
|
||||
```
|
||||
- Mental %: 17% of 240 → 10% (24) + 5% (12) + 2% (4.8) ≈ 40.8
|
||||
- Growth: $100 → $150 over 5 yrs → 1.5^(1/5) ≈ 1.085 ≈ 8.5% CAGR
|
||||
- Breakeven: FC / (P - VC)
|
||||
- Return on investment: ΔProfit / Investment
|
||||
```
|
||||
|
||||
| 날짜 | 변경 내용 | 처리 방식 | 신뢰도 |
|
||||
|------|-----------|-----------|--------|
|
||||
| 2026-05-08 | P-Reinforce Phase 1 정규화 (frontmatter + 헤더 표준화) | UPDATE | A |
|
||||
## 매 결정 기준
|
||||
| 상황 | Approach |
|
||||
|---|---|
|
||||
| Preparing for McKinsey 2026 | Practice Solve (game), not legacy PST |
|
||||
| BCG / Bain interview | Online case + structured case method |
|
||||
| Improve general problem-solving | MECE + hypothesis tree drills |
|
||||
| Ambiguous business problem | Issue tree → 80/20 → hypothesis test |
|
||||
| Quantitative case (sizing) | Top-down + bottom-up cross-check |
|
||||
|
||||
**기본값**: MECE issue tree → hypothesize → data → synthesize. Format-agnostic.
|
||||
|
||||
## 🔗 Graph
|
||||
- 부모: [[Consulting]] · [[Structured-Thinking]] · [[Problem-Solving]]
|
||||
- 변형: [[McKinsey-Solve]] · [[BCG-Online-Case]] · [[Case-Interview]]
|
||||
- 응용: [[Business-Analysis]] · [[Strategy-Consulting]]
|
||||
- Adjacent: [[MECE]] · [[Hypothesis-Tree]] · [[Pareto-Principle]] · [[Market-Sizing]]
|
||||
|
||||
## 🤖 LLM 활용
|
||||
**언제**: structured business analysis, case interview prep, strategic decomposition of ambiguous problems.
|
||||
**언제 X**: technical engineering decisions (use systems thinking instead), pure math optimization.
|
||||
|
||||
## ❌ 안티패턴
|
||||
- **Boiling the ocean**: not 80/20 — analyze every branch equally.
|
||||
- **Non-MECE buckets**: overlapping or missing categories.
|
||||
- **Hypothesis without test**: "I think X" w/ no falsification plan.
|
||||
- **Data dump**: charts without "so what".
|
||||
- **Memorizing PST questions**: format is gone since 2019.
|
||||
|
||||
## 🧪 검증 / 중복
|
||||
- Verified (McKinsey careers website, Minto Pyramid Principle, Case in Point textbook).
|
||||
- 신뢰도 A.
|
||||
|
||||
## 🕓 Changelog
|
||||
| 날짜 | 변경 |
|
||||
|---|---|
|
||||
| 2026-05-08 | Phase 1 |
|
||||
| 2026-05-10 | Manual cleanup — PST history + transferable structured thinking skills |
|
||||
|
||||
Reference in New Issue
Block a user