But, I would counter that what they do is to hide how wide the gap is between the top quartile and the top percentile

Statistical robustness is about finding metrics that can handle tail events, large deviations, without changing much. And, for measuring venture returns, the median is used vs. the mean because it is more robust as a statistical estimator. It represents the typical outcome (i.e., middle-of-the-road funds) vs. the mean that is sensitive to outliers... See more

It gives the industry the credit to be mediocre, blinding allocators to the outlier returns and characteristics of outlier funds.

• First, one can fundraise once their portfolio gets a few markups. So, the fund is top quartile half the time, but only fundraises in periods where it’s top quartile.


• Second, one can be a little bit richer in portfolio valuation, saving the markdowns to post-fundraise. As they know benchmarks, they can add a few touches here and there to become top

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In statistical distributions with thick tails, as Nassim Taleb says, the tail wags the dog . The relevant information is all in the tails, not in the body. Why would an allocator want statistical estimators to ignore the tail and instead overweight the center of the distribution, which is predominantly noise?