AIAP Topic Courses·Management Consulting·07:25

Part 9: Hypotheses, and Gathering Day-One Evidence

Course instructor (unnamed, ex-McKinsey) Main trainer - IIT Madras engineer, ISB MBA; ITC, Boston Scientific, Paytm, ~4.5 years at McKinsey, now product partnerships at LinkedIn

▶LMS lesson

The short version

A hypothesis is 'a proposed explanation for a specific problem based on early evidence' - a starting point, not an answer; your duty is to try to disprove it and prune branches until the answer stands by elimination (0:00-0:02).
Day one at McKinsey felt like 'a shot in the dark'; the cure is a little orientation. Three evidence sources, each turned into a client question: internal benchmarks vs industry (truck age, mileage, fleet investment in the annual report), trend lines (did servicing go from six-monthly to yearly?), comparison with other divisions (if ten factories deliver on time with the same trucks, it is not the trucks) (0:02-0:07).

At a glance, three clicks deep

Skim here first: the closed row is the glance, open is the study card with the key points and timestamps, and the ↓ link drops to that concept's full write-up below.

01The hypothesis, and three sources of day-one evidenceHypothesise -> try to disprove -> prune;›

Hypothesise -> try to disprove -> prune; benchmarks, trends, comparisons.

Hypothesis (0:00-0:02)

Evidence (0:02-0:07)

↓ Full write-up of this concept

The concepts in full

The hypothesis, and three sources of day-one evidence

If ten other factories are on time with the same trucks, the trucks are innocent. One comparison just pruned a branch.

A hypothesis is a proposed explanation based on early evidence - a starting point for investigation. Try to disprove it; when it dies (the trucks are top-notch) form the next one, and by crossing off issue-tree branches you may reach the answer by elimination. Orientation on day one: 1) internal benchmarks vs industry (truck age, mileage, capacity; annual-report fleet spend); 2) the trend line (did performance decline when servicing moved from six-monthly to yearly?); 3) comparison with other divisions (same trucks, on time elsewhere -> location or pre-road issue). Each source becomes a question to ask the client. 'The importance of asking good questions.'

Why it matters

Turns a guess into a testable claim with a plan to test it.

This idea elsewhereparallelCatalyst's evidence ladder

Tools referenced

Tool	Coverage	Moment	Context
McKinsey	mentioned

Action items

Resources mentioned

Resources

docLesson page on the platform

Extraction notes

This page was built from an auto-generated transcript, which garbles product and people's names. Those were corrected silently in everything above and logged here for transparency. The warnings flag claims that were true on the recording day but change fast.

Transcript corrections applied

The transcript says	The trainer actually means
forms	firms
me see / messy	MECE
PNL	P&L

True on recording day — verify before relying

Evergreen method