if it’s based on an approximated normal distribution, then it is entirely possible to have people well into the tail ends. Regardless of the current population of humanity.
Say you have a test with 100 questions and the mean score is 76, but the standard deviation is super low like 3, then getting a perfect score would put you at a z-score of 8, which would be roughly a 1 in 803.7 Trillion rarity.
You don’t need 803.7 Trillion people to take the test to reach this conclusion. In fact (if I’ve done my math correctly) you would only need a sample size of 65 people (including the outlier), to get this though that’s under perfect conditions assuming everyone else got exactly 76. (You’d need a lot more samples in different places to be confident in that result)
Anyway, that’s a super idealized example, but the principle of the Central Limit Theorem is sound and does allow for crazy seeming probabilities. It’s not comparing your score to everyone who has taken the test for quartiles, it’s telling you how you’d compare to everyone who could possibly ever take the test.
The statistical approach is sound; however the test and sampling is not. IQ score tests are just biased, inaccurate, not really scientific, not useful and typically only serve to give people ego boosts.
I never debate the ability of IQ tests to measure what they say they do. That’s a position of automatic loss.
However I will debate any claim of an IQ over 201.
You proposed a test with an absurdly low SD. Can you find one that is respected (maybe MENSA qualifying) that meets that criteria. We can create math problems to invent a hypothetical test that makes getting an IQ over 201 but the second step is to confirm that one not only exists but is credible within the sloppy credibility standards that exist within the industry.
This is describing a defective test. Any test that allows for an IQ above the population limit is a defective test.
My example may have been idealized, but it doesn’t apparently matter. Looks like the raw scores for modern IQ tests are transformed to fit a normal distribution with mean 100 and standard deviation 15. (Meaning they basically subtract the average score from raw scores, divide by the deviation of the raw score, multiply by 15, and add 100. Basically they just scale the data from every test so number of questions doesn’t really matter. This also might reduce bias from people in a given location or who took a specific test if those are the groups for normalization).
The only real question for confidence in the scale is then the number of people who have taken the test. So let’s say we want to be 8sigma sure (likelyhood were wrong is about 1x10^-15) that a person’s IQ is correct to ±1 point.
For this confidence interval we have 8 as the critical z value and 15 as the standard deviation. A 1 point error in score means we’d only need a sample size of 14,400 people.
In other words, you only need to have 14,400 people take the test (or an equivalent one with the same normalization) in order to dettermine with ~99.999999999999999% confidence that someone’s score is between 200 and 202.
I’d imagine that’s not an unreasonable number of samples for MENSA or WAIS. Ergo IQ scores of extraordinarily high values are not necessarily signs of defects in the math of the test.
Given the world population there is zero reason to go to sigma 8. That’s more than 12000x the population of earth.
But, anyway. If the theoretical limit to IQ for the current population is 201 and a test even has the potential to give a result of 202 the test is defective. And people that make IQ tests have to know that because any result outside of theoretical limits is going to get them laughed out of town.
You mentioned Wikipedia earlier. The test that gave the 210 result is known to be defective.
if it’s based on an approximated normal distribution, then it is entirely possible to have people well into the tail ends. Regardless of the current population of humanity.
Say you have a test with 100 questions and the mean score is 76, but the standard deviation is super low like 3, then getting a perfect score would put you at a z-score of 8, which would be roughly a 1 in 803.7 Trillion rarity.
You don’t need 803.7 Trillion people to take the test to reach this conclusion. In fact (if I’ve done my math correctly) you would only need a sample size of 65 people (including the outlier), to get this though that’s under perfect conditions assuming everyone else got exactly 76. (You’d need a lot more samples in different places to be confident in that result)
Anyway, that’s a super idealized example, but the principle of the Central Limit Theorem is sound and does allow for crazy seeming probabilities. It’s not comparing your score to everyone who has taken the test for quartiles, it’s telling you how you’d compare to everyone who could possibly ever take the test.
The statistical approach is sound; however the test and sampling is not. IQ score tests are just biased, inaccurate, not really scientific, not useful and typically only serve to give people ego boosts.
I never debate the ability of IQ tests to measure what they say they do. That’s a position of automatic loss.
However I will debate any claim of an IQ over 201. You proposed a test with an absurdly low SD. Can you find one that is respected (maybe MENSA qualifying) that meets that criteria. We can create math problems to invent a hypothetical test that makes getting an IQ over 201 but the second step is to confirm that one not only exists but is credible within the sloppy credibility standards that exist within the industry.
This is describing a defective test. Any test that allows for an IQ above the population limit is a defective test.
My example may have been idealized, but it doesn’t apparently matter. Looks like the raw scores for modern IQ tests are transformed to fit a normal distribution with mean 100 and standard deviation 15. (Meaning they basically subtract the average score from raw scores, divide by the deviation of the raw score, multiply by 15, and add 100. Basically they just scale the data from every test so number of questions doesn’t really matter. This also might reduce bias from people in a given location or who took a specific test if those are the groups for normalization).
The only real question for confidence in the scale is then the number of people who have taken the test. So let’s say we want to be 8sigma sure (likelyhood were wrong is about 1x10^-15) that a person’s IQ is correct to ±1 point.
For this confidence interval we have 8 as the critical z value and 15 as the standard deviation. A 1 point error in score means we’d only need a sample size of 14,400 people.
In other words, you only need to have 14,400 people take the test (or an equivalent one with the same normalization) in order to dettermine with ~99.999999999999999% confidence that someone’s score is between 200 and 202.
I’d imagine that’s not an unreasonable number of samples for MENSA or WAIS. Ergo IQ scores of extraordinarily high values are not necessarily signs of defects in the math of the test.
Given the world population there is zero reason to go to sigma 8. That’s more than 12000x the population of earth.
But, anyway. If the theoretical limit to IQ for the current population is 201 and a test even has the potential to give a result of 202 the test is defective. And people that make IQ tests have to know that because any result outside of theoretical limits is going to get them laughed out of town.
You mentioned Wikipedia earlier. The test that gave the 210 result is known to be defective.