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8) The key insight: you're not choosing between looking at statistics or acing randomly. You have a prior coming in: based on your intuition and critical thinking. The question is whether data is more or less useful than your priors, and whether you combine them well.

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9) In Bob's case, his data is technically correct! But there are two core issues: a) his revenue might not be the same in both cases; maybe the mobile app makes more than 4x as much revenue as the B2B product. b) also: valuation isn't all that matters! I'd prefer earnings.

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10) Bob probably would have been better just saying "let's build the business that seems the best" and ignoring valuation. In Alice's case, her data is probably being misinterpreted.

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11) Yes, there is a positive correlation between having 10k employees and being successful: You can only hire 10k employees if you've done well. So there's a correlation, but the direction of causation is probably wrong.

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12) And how about Zed? Well, what, in the end, is an impression? One of the important properties about superbowl ads: they're talked about again and again and again, in lots of places that are hard to track. The direct views significantly underestimate it's impact.

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13) And in this case, a simple gut check might have made Zed realize that _obviously_ superbowl ads have large impact, and a lot of that is the chatter. So there are lots of ways to use data poorly. That doesn't make it useless--there are also lots of ways to use it well!

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14) But if you do a mediocre job of using data, it just adds noise which distracts you from your baseline reasonable judgement. There is a fairly high bar that statistical analysis has to overcome to be net useful!

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Beware The Man Of One Study

15) And as our world becomes richer and richer in data--and as it becomes more commonplace to use it and cite it--it's getting misused more and more. (see also slatestarcodex.com/2014/12/12/bew)

slatestarcodex.com

Aquinas famously said: beware the man of one book. I would add: beware the man of one study. For example, take medical research. Suppose a certain drug is weakly effective against a certain disease…

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16) And this is a failure mode that a _lot_ of people fall into. The vast majority of statistics that I see quoted are useless. The times when stats are more likely to be useful are when they are answering a very specific, intentional question.

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12:52 AM · Mar 26, 2022Twitter Web App

17) If you've thought hard about a decision you have to make and think you really understand the various factors, and know which factor you're uncertain about, then it can be *extremely* helpful to get some data!

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18) But aimlessly generating data just distracts. It's also very similar to a trap that some interview candidates fall into, particularly those with strong math backgrounds: Given a hard, messy question, they'll try to solve it exactly. And if they can't, they get flummoxed.

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