I'm not saying the ranking is necessarily wrong, but that turning the ranking into a distribution is constructed. And it MIGHT be a correct construction, but I am less confident that is true.

The distribution implies something like "someone at 50% is not that different than someone at 55%" but "someone at 90% is very different from 95%". That is: the x axis implies there's some unit of intelligence, and the actual intelligence of people in the middle is roughly similar despite ranking differences. That distribution also implies that when you get to the extremities the ranking reflects greater differences in intelligence.

The distribution implies that a score of 100 means you did better than half the population, and that a score of 130 means you did 2 standard deviations better than the population ie. better than 95% of other people. We have no objective measure of IQ so we use relative rankings. If you used a uniform distribution for iq everyone currently above 145 would have 99 out of 100 IQ. Normal distribution is useful when you want to differentiate points in the tails

It does seem like you should assume the accuracy of the result decreases as you get away from the norm of an IQ test, though I have no idea if it's been validated. But particularly if there are mistakes on the test questions or any kinds of ambiguity in any of the questions, it seems like you'd expect that.

Like if you have two different IQ tests and someone takes one, and gets 100, if 100 is normed to the 50th percentile, maybe you have 95% confidence that on the next test they're also getting 100 +/- 2.5. But if they get 140, that's normed to like 99th percentile, maybe your 95% confidence interval for the next test is 140 +/- 12.5. (I really don't know, I just suspect that the higher the percentile someone gets, the less confidence you'd have and mostly know stats from physical and bio science labs, not from IQ or human evaluation contexts.)