Hi everyone,

I want to address a common rebuttal which I have observed with increasing frequency lately. It goes something like this: “Yesterday I went to the store and I saw a bunch of average dudes with average girlfriends, even a short bald guy with someone totally out of his league. See, everything is fine!”. Or something along these lines, you know what I mean.

Strangely enough this observation is actually 100% correct. But not because the world is just and everyone lived happily ever after, but rather because it is the direct result of a statistical phenomenon which is described by the Central Limit Theorem.

“In probability theory, the central limit theorem (CLT) establishes that, in many situations, for independent and identically distributed random variables, the sampling distribution of the standardized sample mean tends towards the standard normal distribution even if the original variables themselves are not normally distributed.” ( wiki )

In other words, let's assume we have a random variable X which represents a match, a pairing between two people which is drawn from a distribution representing the likelihood of said pairing. We then take the differences between the (somewhat)quantifiable stats of those people who matched. ie looks, socioeconomic status, social skills etc. and record it.

If this process is repeated for many iterations( even including unmatching and redrawing, because of a breakup/divorce, rejection, whatever... ) for a sample set which converges to the set representing the entire population, the resulting distribution of the recorded differences will tend to a normal distribution. Hence for the vast majority of members of the set, the differences will only differ slightly from the mean( +- 1 SD ). This is exactly what is observed in real life when it comes to the general population: Most people end up with someone of similar economic status, looks, values etc.

Now, here comes the good part. If you read the definition again, you will notice that the distribution we draw our pairing from does NOT need to be normally distributed itself. Hence it could be completely skewed and unevenly distributed. Even if we assume the most extreme case and say the likelihood of a match depends on looks alone, and we draw the match from something which models something unfair like the infamous 80/20 distribution, the described statistical process will converge to the standard normal distribution regardless! The opposite is also true. You draw from what is essentially just an equal likelihood for everyone, independent of anything( not even personality… ) and the result would still be the same. It does not matter, it will always converge.

So in a strange twist, the red, blue, whatever-pill assumptions are all correct within the given model, and will in time result in a similar snapshot of society.

Hence one could argue that the observation “I see average dudes with looks-matched average gfs all the time” and "women only going for the top percentile of men" can both be true and run adjacent to one another. The statement is therefore essentially meaningless and ultimately just trivial.

It says nothing about how we got there in the first place, which then brings us back to the same starting point of red vs. blue pill. “No new knowledge can be extracted from this telling. This confession has meant nothing.” ;)

Thank you for reading and have a great day.

PS: If you want to learn more about CLT here is a neat video explaining it in more detail:

https://www.youtube.com/watch?v=zeJD6dqJ5lo