Many candidates are obsessed with generating correct answers in estimations they must make within cases or standalone estimation cases. This is a poor strategy. By obsessing about the final answer in a McKinsey estimation case, they ignore the structure of the estimation case which is far more important and forget why an estimation case exists in the first place – to test the ability to generate an answer with imperfect information. Listeners are strongly advised, as well, to ignore speed at the beginning and focus on good case technique.

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2 responses to Estimation Cases Should Ideally Be Imprecise

  1. SZ,

    You are not being picky since they are different, and the wording is chosen carefully to make that distinction. Consultants strive to be accurate and good enough on precision.

    What is the point of given a number accurate to 2 decimal places when it is so far off the mark? Do you think the client values that level of precision in being incorrect?

    The answer to this is fairly obvious.

    Second, math skills are overrated and not what we look for. If we looked for math skills then MIT graduates would dominate management consulting. They do not.

    We look for critical reasoning and analytic skills which are mutually exclusive from being skilled at math.


  2. Hi Michael, thanks for this.

    Just to be picky on the wordings used, don’t you think candidates should strive to be precise, and yet not worry about being accurate? After all precision reflects math skills, doesn’t it? E.g. The number of registered cars in the US at 102.80 million is a precise answer, yet far from being accurate (~254 million)

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