I used to over-explain every step until a peer (a former consultant) told me to stop ‘narrating the math.’ Their candid feedback forced me to strip my answer to essentials: one-sentence market definition, two numbered assumptions, quick arithmetic, and a sanity check. That discipline reduced my rambling and made interviewers actually engage. Curious: what blunt feedback changed your approach the most, and how did you adapt it?
people love their grand assumptions. a blunt critique i heard once: ‘your model smells like optimism.’ it stung, but it forced me to swap wishful numbers for defensible ones. now i always give a worst/best case split and pick the conservative headline. nobody claps for optimism in a case interview; they clap for defensible thinking. own the conservative call and you look competent, not dreamy.
a vet told me to stop treating market sizing like a TED talk. cut the storytelling, show the chain of logic, and move on. if the interviewer wants detail they will ask. the brutal truth: time is the interviewer’s currency, not yours. spend it smartly.
i got told to stop apologizing for assumptions — huge. now i state them confidently and it feels so much better. still nervous tho but getting there!
small win: after a guy in the forum said ‘use round numbers’, i stopped digging for extra precision. saves me time and i sound cleaner.
q: any tips to keep calm when a vet starts poking at one assumption? i freeze sometimes.
Direct feedback is invaluable because it reveals consistent weak spots: over-justifying trivial choices, failing to quantify assumptions, and neglecting a sanity check. I advise candidates to practice short, assertive assumption statements and pair each with a quick source or a comparative check. For instance, if you estimate average spend, compare to GDP per capita or a known category spend. This habit converts vague claims into defensible estimates quickly.
When colleagues give blunt critiques, treat them as experiments. Implement one piece of feedback at a time—say, cutting your assumptions from five to two—and measure the effect in mock interviews. Often the problem isn’t the math but the delivery: concise, numbered statements improve perceived competence more than perfect precision.
I had a teammate who would scribble one-line comments like ‘unfounded’ or ‘source?’ during my practice runs. That micro-feedback forced me to either cite a quick rationale or drop the assumption. Over time it retrained my habits: fewer wild guesses, more quick validations. It transformed my approach more than any tutorial.
From a measurable standpoint, candid peer feedback reduced my average case length by 20% while keeping answer quality stable. The key changes were: explicitly stating assumptions, using rounded figures for speed, and ending with a single validation. Track a few metrics during practice—time to first assumption, number of assumptions, and whether you include a sanity check—and iterate based on peer critique.
If you want structure for peer feedback sessions, record three timed mocks and note recurring comments. I logged feedback tags and found the top two were ‘unsupported assumption’ and ‘no sanity check.’ Targeting those reduced negative feedback frequency by half. Use peers to point out patterns, then practice the concise fixes until they become automatic.