Hypothesis-Driven Problem Solving

Concept

Don't boil the ocean. Start with a hypothesis — a specific, falsifiable claim about why the problem exists — then identify exactly what data would confirm or refute it. This is how consultants avoid running 40 analyses when 2 would do.

The visual trick: a comparison table that shows what you'd expect to see if the hypothesis is true next to what the data actually shows (or what data is required if not yet collected). The point of divergence is where the answer lives.

Required output format

1. A one-sentence falsifiable hypothesis prefixed **Hypothesis:**.
2. A Markdown table with columns: Variable | Expected (if hypothesis true) | Actual / Required Data.

**Hypothesis:** [One-sentence falsifiable claim.]

| Variable | Expected (if hypothesis true) | Actual / Required Data |
|---|---|---|
| Variable 1 | Expected pattern | What we observe / need |

Defaults & flex points

Default	When to flex
4–7 variables	If only 3 truly diagnostic variables exist, use 3. Don't pad.
At least one control row	Don't flex this — confirmation bias is the most common failure mode.
One hypothesis per table	If multiple hypotheses, output multiple tables.
Variables are measurable	Don't flex this — unmeasurable variables aren't testable.

The control row test: at least one row should describe something that should NOT match if the hypothesis is true. If everything in your table is set up to confirm, you're not testing — you're rationalizing.

Example — pricing

Problem: A B2B software company raised list prices 18% in January. Six months in, new-logo bookings are down 22%.

Hypothesis: The price increase is the dominant driver of the bookings decline (rather than competitive shifts or pipeline quality issues), and the impact is concentrated in deal sizes under $50K where price sensitivity is highest.

Variable	Expected (if hypothesis true)	Actual / Required Data
Win rate by deal size band, Q1 vs. Q3	Should drop sharpest under $50K, modest under $250K, near-flat above $250K	Need to pull from CRM — segment win rates by ACV band before/after Jan 1
Stage where deals stall	Late-stage stalls (procurement / pricing review) should rise; early-stage should be flat	Q3 stage drop-off: late-stage stalls 38% (Q1: 12%) ✅ matches
Discount approval rate	Sales should be requesting more discount approvals to close deals	Discount-request volume up 2.4× ✅ matches
Competitive losses citing price	Win/loss survey should show "too expensive" rising as a top-3 reason	Q1: 14% → Q3: 41% ✅ matches
Win rate above $250K (control)	Should be near-flat — large deals less price-sensitive	Q1: 28% → Q3: 26% — near-flat ✅ supports hypothesis
Pipeline quality score (control)	Should be unchanged — would invalidate hypothesis if it shifted	MQL → SQL conversion stable; no quality shift ✅ supports hypothesis
Recent competitor pricing moves	Should show no major change	Two competitors held prices; one raised 10% (similar trajectory) ✅ supports hypothesis

The two control rows (>$250K win rate, pipeline quality) are designed to invalidate the hypothesis. They didn't — that's strong evidence. Without them, the table would just be confirmation bias.

Common mistakes

• Vague hypothesis. "We have a sales problem" — there's nothing to test.
• No control rows. Every variable set up to confirm. The hypothesis becomes unfalsifiable.
• Cramming multiple hypotheses into one table. Output two tables instead.
• Burying the conclusion. If actuals all match, say so explicitly. Don't make the reader infer.

When to use this skill alone vs. as part of the full analysis

• Standalone: when you already have a candidate hypothesis and need a clean test plan.
• As part of full analysis: strategy-consultant — hypothesis testing is step 3 of 5. Run mece-framework and issue-trees first to surface the hypotheses worth testing.

---

Acknowledgment & License

Adapted from Analyst Academy on YouTube — 5 Consulting Frameworks to Solve Any Problem. MIT-licensed; see LICENSE.