Uniqueness Bias: Why it matters, how to curb it

That makes a lot of sense to me, because I wrote a textbook in about 2 years. But...

There are textbooks and there are textbooks. My book covers exactly what I need for my class, has no publisher (students download it from the class website), and doesn't have a lot of the bells and whistles that a publisher would demand. I can easily see how a "real" textbook would take over 3x as long.

So part of the lesson of the authors' textbook is a standard software development lesson - there's a huge gap between a working prototype and a shipping product, and even if that prototype does everything you think the product needs to do, (a) you're wrong, and (b) you may be less than halfway to the point where you can ship.

> we conclude from the available data that [...] perceived and actual uniqueness are uncorrelated

(Though as other comments suggest, their method of arriving at this conclusion might be flawed.)

It's probably best to think of an initial budget as a foothold, and that its ideal amount is low enough to be approved, but high enough to prevent the organization from changing direction after realizing that it's not going to be nearly enough i.e. to think in terms of "pot-commitment."

Much as with reading and writing, I think it takes an active imagination and a long slog of unlearning to trust logic (the "ought to" thinking that shields one from reality) and coming to terms with the race not being to the swift, etc, and that these effects can be quantified.

It's not that some people are incapable of it. Much like literal literacy has reached rates of 99.9 % in parts of the world, I'm convinced statistical literacy can too. But when your teacher is not statistically literate (which I hypothesise they are not, generally speaking), they will not pass that on to you. They will not give you examples where the race is not to the swift. They will not point out when things seem to happen within regular variation and when they seem to be due to assignable causes. They will not observe the battle against entropy in seating choices in the classroom. They will not point out potential confounders in propensity associations. They will not treat student performance as a sample from a hypothetical population. They will not grade multiple-choice questions on KL divergence, although that would be far more useful. I could go on but I think you get the point.

Yet to be clear, I'm not talking about just applying statistical techniques and tools. I'm talking about being able to follow a basic argument that rests on things like "yes I know they are a fantastic founder but startups fail 90 % of the time and so will they" or "if the ordinary variation between bus arrivals is 5–15 minutes and we have waited 20 minutes then there is something specific that put the bus we are waiting for into a different population." These are not obvious things to many people.

This is not a personal failure – it is a lack of role models and teachers. I wouldn't have considered myself statistically literate until recently, and only thanks to accidentally reading the right books. I wouldn't even have known what I was missing were it not for that!

I suspect it will take a few generations to really get it going.

If someone would donate me large amounts of money I would love to actively research this subject, come up with reliable and valid scales to measure statistical literacy, and so on. But in the meantime I can only think in my spare time and sometimes write about it online.