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