Sociobiology
Fuchsia
- Joined
- Apr 9, 2022
- Posts
- 10,969
- Reputation
- 12,348
TL;DR: Use a standard distribution and measure people using deviations from the mean.
Introduction to Normal Distribution
This is a normal distribution, otherwise called a bell curve or gaussian distribution. It is one the consistent and replicable phenomenon in the measurement of natural phenomenon. Height, IQ, weight, muscle mass, facial ratios, and thousands of other traits follow this pattern. When you repeatedly measure a trait, the majority of the traits will cluster together.
As you can tell by the chart, 68.2% of people willf fall with 1 standard deviations of the mean either side, and expotentially less people are occupy standard deviations away from the mean.
Attractiveness should follow the a normal distribution if enough people are measured. Any deviation in rating standards from the mean is statistical noise. If a woman for some reason rates the average man as a one, firstly she is mentally ill, and also her dating standards deviate heavily from how the larger whole of women rate men.
Applying SD to Attractiveness
A fair rating system should be from 1-9, with a mean of 5, and a standard deviation 1. If you look on the chart, the weird u, is called mu, and represents the mean, the weird o represents sigma, and represents the standard deviation.
So, according to the previous rules, the majority of people fall between 4 and 6 in a fair rating system, which is 5.52 billion people (men and women). This means that to be the top 20% in attractiveness you have to 0.84 SD away from the mean, or be a part of 1.62 billion people. Being 0.84 SD away from the mean, is as rare as having light eyes of any colour for any sex, or being a man above the height of 6"0+.
Let me put whole of the attractiveness scale into perspective. Being in between the mean and 1 SD away from the mean is as rare as having Blood Type-O, approximately. Being 1 SD to 2 SD is as rare as being born in June, approximately. Being 2 SD to 3 SD is as rare as finding a left-handed woman, approximately. Being 3 SD and further is as rare as flipping heads with a coin 10 times over, or having red hair and green eyes together, approximately.
Being a man who 6ft+, blonde hair, and blue eyes, is extremely rare globally, occuring in around 0.027% of men globally, or in the 3 SD - 4 SD range, or 1 in 3700 men. However, in the Netherlands, the trait is much much more frequent, the combination occurs 9.6% within the Netherlands, or 1 in around 10 men, or 1.31 SD from the mean. You must understand how results for any trait or not distributed equally across the world. You may live in the part of the world that has a high number of slayers, this does not mean people where you live are being overrated, instead it means you live in an area with lots of slayers.
Now, how does this relate to ratings? People should be rated using SD ranges. If you remember that a fair system used 5 as a mean with a standard deviation of 1. It would be more accurate, for example, to measure a person as: mean -1SD, rather than 5.5PSL. If a rater is unsure where a person should be rated then a wider SD range would be given, if a rater is certain where a person should be rated then a smaller range would be given. For example, -2 SD to +1 SD or +2.2SD to +2.3SD.
Facial Ratio Analysis Method
Now, you may be wondering, how do I apply this knowledge to mathematical facial analyses? For every measurement, in mm, subtract the measurement of the face analysed from the mean perfect score, this should result in a disharmony score. Then add all the disharmony scores together the divide this score by the number of measurements taken, this is the true disharmony score of the face. Use a one-tailed normal distribution (someone else can make this chart I cba), the mean is an absolute perfect score on this one-tail distribution and deviations away from it equate to penalty points. You want to create a collection of these one tailed normal distributions for all traits you can think of. These penalty points are in SD, and at the end, you want to subtract all of these SD penalty points from a perfect score on a new chart, of lets say 9 or 10, the result of this should leave you with your true attractiveness score.
I cba to make all these graphs, a forum autist can collect the data and plot them instead.
If anyone wants to turn this post into a YT video, you have my consent, creative commons.
Introduction to Normal Distribution
This is a normal distribution, otherwise called a bell curve or gaussian distribution. It is one the consistent and replicable phenomenon in the measurement of natural phenomenon. Height, IQ, weight, muscle mass, facial ratios, and thousands of other traits follow this pattern. When you repeatedly measure a trait, the majority of the traits will cluster together.
As you can tell by the chart, 68.2% of people willf fall with 1 standard deviations of the mean either side, and expotentially less people are occupy standard deviations away from the mean.
Attractiveness should follow the a normal distribution if enough people are measured. Any deviation in rating standards from the mean is statistical noise. If a woman for some reason rates the average man as a one, firstly she is mentally ill, and also her dating standards deviate heavily from how the larger whole of women rate men.
Applying SD to Attractiveness
A fair rating system should be from 1-9, with a mean of 5, and a standard deviation 1. If you look on the chart, the weird u, is called mu, and represents the mean, the weird o represents sigma, and represents the standard deviation.
So, according to the previous rules, the majority of people fall between 4 and 6 in a fair rating system, which is 5.52 billion people (men and women). This means that to be the top 20% in attractiveness you have to 0.84 SD away from the mean, or be a part of 1.62 billion people. Being 0.84 SD away from the mean, is as rare as having light eyes of any colour for any sex, or being a man above the height of 6"0+.
Let me put whole of the attractiveness scale into perspective. Being in between the mean and 1 SD away from the mean is as rare as having Blood Type-O, approximately. Being 1 SD to 2 SD is as rare as being born in June, approximately. Being 2 SD to 3 SD is as rare as finding a left-handed woman, approximately. Being 3 SD and further is as rare as flipping heads with a coin 10 times over, or having red hair and green eyes together, approximately.
Being a man who 6ft+, blonde hair, and blue eyes, is extremely rare globally, occuring in around 0.027% of men globally, or in the 3 SD - 4 SD range, or 1 in 3700 men. However, in the Netherlands, the trait is much much more frequent, the combination occurs 9.6% within the Netherlands, or 1 in around 10 men, or 1.31 SD from the mean. You must understand how results for any trait or not distributed equally across the world. You may live in the part of the world that has a high number of slayers, this does not mean people where you live are being overrated, instead it means you live in an area with lots of slayers.
Now, how does this relate to ratings? People should be rated using SD ranges. If you remember that a fair system used 5 as a mean with a standard deviation of 1. It would be more accurate, for example, to measure a person as: mean -1SD, rather than 5.5PSL. If a rater is unsure where a person should be rated then a wider SD range would be given, if a rater is certain where a person should be rated then a smaller range would be given. For example, -2 SD to +1 SD or +2.2SD to +2.3SD.
Facial Ratio Analysis Method
Now, you may be wondering, how do I apply this knowledge to mathematical facial analyses? For every measurement, in mm, subtract the measurement of the face analysed from the mean perfect score, this should result in a disharmony score. Then add all the disharmony scores together the divide this score by the number of measurements taken, this is the true disharmony score of the face. Use a one-tailed normal distribution (someone else can make this chart I cba), the mean is an absolute perfect score on this one-tail distribution and deviations away from it equate to penalty points. You want to create a collection of these one tailed normal distributions for all traits you can think of. These penalty points are in SD, and at the end, you want to subtract all of these SD penalty points from a perfect score on a new chart, of lets say 9 or 10, the result of this should leave you with your true attractiveness score.
I cba to make all these graphs, a forum autist can collect the data and plot them instead.
If anyone wants to turn this post into a YT video, you have my consent, creative commons.
Last edited:
