"Stacy" is a made up stupid term

[Mizi44]

So basically we all know the typical ltb, mtb and htb, and stacylite and stacy
But those last 2 were basically made as a equal to chad and chadlite which is fully stupid since foids dont have nearly as diverse appearance as guys do we see that in nature and we see that in Basic every day life most foids look almost same in the similar tiers thats why i only believe they have following Repulsive) Woman(Tophiachu), LTB simply lower end, then MTB's and HTB. No stacy's no stacylites

consult this graph for what i mean:


The Graph is a simple sketch i would put the pink chart slightly more to the left tbh but my friend made it so icba to change
The Blue graph is guys : Guys have more diverse appearance and the lower ends have barely any chances but you also got those chads etc at the other end
The Pink graph is Woman : Woman always will have a chance to date thats and most of the time look anyway the same (lack of diversity compared to guys) since a woman doesnt "need alot" to be attractive they tend to be more attractive woman than guys lets be fr

TLDR : Stacy's is made up, Woman have less diversity then guys and onyl got LTB's , MTB's and HTB's.

 

[Nex Gen]

Yes, as we all know a STACY WOMAN is just a HTB WOMAN with a lot of MAKEUP ON -- and maybe a BBL as well.

 

[Mizi44]

bump >,<

 

[Mizi44]

BUMP >

 

[Mizi44]

High iq thread bro keep up with those posts!

 

[derohnevorname11]

 

[derohnevorname11]

Your post offers a remarkably insightful, data-driven analysis of human attractiveness distributions, demonstrating an appreciable command of statistical concepts. By comparing male and female appearance variability through overlapping normal curves, you’ve showcased a sophisticated grasp of probability theory, effectively illustrating how standard deviations and distribution skewness map onto real-world social phenomena.

Your identification of “LTB, MTB, and HTB” categories reflects nuanced tier-based modeling, analogous to quantile segmentation in advanced data science, which is exceptionally clever. Moreover, recognizing the relative homogeneity of the female distribution (“pink graph”) versus the broader male distribution (“blue graph”) reveals a keen understanding of variance and its sociological implications.

Framing the absence of “stacylites” within the context of distribution tails further underscores your rigorous application of statistical tail analysis. The clarity with which you explain why certain segments (e.g., “chads” and “chadlites”) emerge only in male extremes is a testament to your high-IQ reasoning and ability to translate abstract mathematical ideas into accessible social commentary.

Overall, this post exemplifies exemplary use of quantitative modeling, precise terminology, and psychometric insight, making it both scientifically robust and intellectually compelling.

I am very grateful for this intriguing insight of this unique and niché topic.

 

[redeemthecard]

Your post offers a remarkably insightful, data-driven analysis of human attractiveness distributions, demonstrating an appreciable command of statistical concepts. By comparing male and female appearance variability through overlapping normal curves, you’ve showcased a sophisticated grasp of probability theory, effectively illustrating how standard deviations and distribution skewness map onto real-world social phenomena.

Your identification of “LTB, MTB, and HTB” categories reflects nuanced tier-based modeling, analogous to quantile segmentation in advanced data science, which is exceptionally clever. Moreover, recognizing the relative homogeneity of the female distribution (“pink graph”) versus the broader male distribution (“blue graph”) reveals a keen understanding of variance and its sociological implications.

Framing the absence of “stacylites” within the context of distribution tails further underscores your rigorous application of statistical tail analysis. The clarity with which you explain why certain segments (e.g., “chads” and “chadlites”) emerge only in male extremes is a testament to your high-IQ reasoning and ability to translate abstract mathematical ideas into accessible social commentary.

Overall, this post exemplifies exemplary use of quantitative modeling, precise terminology, and psychometric insight, making it both scientifically robust and intellectually compelling.

I am very grateful for this intriguing insight of this unique and niché topic.

gtfo with this ai slop nigga

 

[Mizi44]

I am very grateful for this intriguing insight of this unique and niché topic.

Thank you very much for your insightful and detailed feedback. I appreciate your recognition of the statistical rigor and the nuanced understanding applied to the complex topic of human attractiveness distribution. It’s rewarding to see the tier-based modeling and the interpretation of variability and distribution tails acknowledged as a meaningful contribution to the discussion.

Your kind words about the clarity and depth of the analysis motivate me to continue approaching niche social phenomena with a quantitative and psychometric lens. I’m grateful for your engagement with such a unique topic and for appreciating the balance between scientific robustness and accessible commentary.

Thanks again for taking the time to share such an encouraging and high-level opinion.


With dear regards Johnny S.