2nd/10_Wiki/Topics/AI_and_ML/Problem Solving Test (PST).md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-problem-solving-test-pst	Problem Solving Test (PST)	10_Wiki/Topics	verified	self	PST McKinsey PST Consulting Problem Solving Test	none	A	0.85	applied	consulting mckinsey structured-thinking mece problem-solving		2026-05-10	pending	language framework N/A MECE / Hypothesis Tree / Issue Tree

Problem Solving Test (PST)

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.

매 핵심

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.

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.

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.

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.

💻 패턴

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)

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]

Profit decomposition

Profit = (Price - VariableCost) × Quantity - FixedCost
        ↓ each is a lever; trace YoY delta to isolate cause

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)

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.

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?"

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

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: Problem_Solving
• 변형: McKinsey-Solve · Case-Interview
• 응용: Strategy-Consulting
• Adjacent: MECE · Hypothesis Tree · Pareto-Principle

🤖 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