Tag Archives: Statistics

1-10 Scale: An Analysis

Last week I wrote about the Archetypical Modern Women. It was my most popular post ever by views and was also one of my most commented posts as well. Most manospherians liked the post, but there was one common criticism that seemed  virtually unanimous: I overrated the woman, she was not a 7. The consensus seemed to be she was a 5, although a couple commented she was a 3 or even lower.

I explained my reasoning in the comments: “she’s thin, young-ish with a moderately cute face. She’s not beautiful, but a youngish, plain sort of pretty with a slim build would fit my definition of a 7.”

I generally don’t use the scale in real life; in fact, I can not remember ever having using it in RL, but there’s a small possibility I have. In normal conversation, the scale is kind of silly; the descriptors of beautiful, cute, unattractive, etc. are usually more than good enough and are more humanizing. (That and a numerical scale sounds sort of spergy, and I have enough problems with that as it is).

On the other hand, I occasionally use it on the blog as it is a simple comparative method; more human descriptor cans be open to interpretation and can have different meanings. While a numerical scale at least gives the illusion of objectivity.

But after the criticism of my assigning the label 7 I wanted to figure this out, my inner data nerd was aroused, so I’m going to analyze this more. I’ll warn you now, this is going to get spergy and is going to be dehumanizingly analytical.

Oh, and before I begin, Truthmosis at RotK has a post up on the scale that I came across while writing this. Check it out.

I’d also like to point out that, to some degree, beauty is subjective, so a numerical scale is not the be-all-end-all of female beauty. There are certain objective metrics of beauty: a 0.7 hip-to-waist ratio, symmetry, and other such indicators of fertility and health, that (almost) all men are naturally drawn towards. These can be a basis for an “objective” 1-10 scale.

But outside of that, there are numerous subjective factors on which men disagree. For example, I really like fair-skinned, light-haired, innocent-looking women (ie. cute women) and detest tattoos and piercings. A tongue piercing disgusts me and is an automatic 3-point drop. So, if I were to rate a woman with a tongue piercing a 5, others who don’t find it disgusting, might rate that woman an 8. Another example: I’ve never figured out why the Captain likes Jennifer Aniston or many men like Angelina Jolie; never seen the appeal.

Anyway, with that caveat out of the way, here we go.

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The first thing to do when creating a scale is decide the system the scale will use. The two major ones are the bell curve and the decile system. Men as a whole tend to use a bell curve system (on a 5-point scale), but I’ve tended to think in a decile system.

In a normal bell curve system (and looks would be normally distributed) a scale would be related to standard deviation. In standard deviation, 68% of all women would fall within one standard deviation from the mean, while 95% of all women would fall within two, and 99% would fall within three.

In a 1-10 scale 5 would be the mean. Most like we’d use 2 sigma (SD:2.5) above the mean to signify a 10 and 2 sigma below to signify a 0. 1 sigma would make far too many 10s, and 3 sigmas would mean only 2% of woman are above a 7+.

A 2 sigma scale would mean means that about 2% of woman would be 10s and 2% would be 0s. About 14% would be 7.5-9.5s and another 14% of woman would be 0.5-2.5s. The vast majority of woman (68%) would be 2.5s-7.5s.

We could also use a 2 sigma to signify 1s and 9s (SD:2). On this scale 2% of woman would be 9+ and another 2% would be below <1. 14% of woman would be 7-9 and another 14% of woman would be 1-3. The large majority of woman (68%) would be 3-7s.

If I were to use a bell curve, the latter is likely the one I would use because no one uses 0 on the 1-10 looks scale and many think (and I agree) that there are no 10s. Limits could easily be put at .1 and 9.9 without negatively effecting the rest of the scale. Not to mention the use of whole numbers rather than decminals greatly simplifies the scale.

So, if we’re scaling women’s looks on a 1-10 (Mean:5, SD:2) we can use a stanine scale to find the proportion of woman at each number.

On the other hand, if we use a decile system 10% of women would be 1s, 10% would be 10s, etc.

The former is more useful for statistical calculation, the latter is easier to use for everyday talk. It is a lot easier to calculate: she’s a 10 because she’s in the top 10% of people, she’s a nine because she’s in the 80-90% range, etc. than it is to calculate: she’s a 9 because she’s 2 SD above the mean and is in the top 4% of woman.

In more practical immediate effect, the former will result in a lot of 4-6s and few 1s and 9s, while the latter will result in an even distribution of all types of woman.

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Knowing this, how can we systematize the calculation of where an individual woman falls on this scale. That”s likely impossible because beauty is to some degree subjective, but we can give it a shot. This analysis will focus on adult women of child-bearing age because menopausal women are no longer sexually attractive.

In the US 32% of women aged 20-39 are obese. If we used the decile system, that would mean the obese take up all of 1s through 3s. If we used the bell curve, the obese take up 1-3 and most of the 4s as well.

But obesity is not the only indicator of unattractiveness, some women just have the bad luck to be born with a deformity of an extremely unattractive face. If, for simplicities sake, we estimated that 8% of women are simply born deformedly ugly (not unattractive or plain, just ugly), that means that on both scales 1-4s are made up of the deformed and fat.

So, simply not being obese or deformed would immediately make a woman a 5 in either scale.

Back to weight, in addition to the the obese are the overweight. 64% of adult women are either obese (BMI >= 30) (36%) or overweight (BMI of 25-29.9) (28%), so we’ll assume the 28% overweight rate hold for women 20-39. So, we now have 60% of women aged 20-39 who are overweight or fat, but let’s remove 5 percentage points because the BMI does sometimes classify fit people with muscle as being overweight. So about 55% of child-bearing age woman are unattractive due to be overweight or obese.

I can not find any numbers on the percentage of woman that are unattractive due to face alone, so I’ll have to make up some assumptions. Let’s assume, for the sake of ease, that 10% of women who are not fat, have faces that are unattractive enough, that a moderately fat woman with a decent face would rate higher on a scale.

With that assumption we now come to 65% of women are either fat or as unattractive as a fat woman.

(Check out this BMI visualizer to understand what is meant by overweight and obese).

In a decile scale that means that a woman who is not fat or equally unattractive is automatically a 7; in a normal distribution scale a woman who is not fat or equally unattractive is automatically a 6.

This gives us a starting base.

I do not have the time or ability to start messing around with the ins and outs of symmetry, eye size, distance between the eyes and mouth, and all the other micro-variations that distinguish beauty. Suffice to say though that most men can tell objective beauty of these micro-variations fairly easily.

So, we can assume they’d mostly agree.

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Based on this here’s a 1-10 scale we can use based on the decile system.

1-4: Obese and/or deformedly ugly
5: Fat or ugly
6: Chubby with a cute face or unattractive
7: Plain, not fat
8: Somewhat attractive
9: Slim and pretty
10: Curvy and beautiful

Here’s on based upon normal distribution:

1-4: Obese and/or deformedly ugly
5: Fat, chubby with an unattractive face, or ugly
6: Plain, not fat or chubby with a cute face
7: Slim and pretty
8: Curvy and beautiful
9: The best of the best (very rare)
10: Does not exist

The normal distribution lumps the middling and moderately attractive categories together but allows for the distinguishment of the really beautiful from the beautiful, while the decile scale allows for more distinguishment from the middling, but lumps all the beautiful together under 10.  The decile system leaves more distinguishment in those of middling beauty, but lumps the good looking into 2 categories.

From the impression I get from people write on the manosphere, they seem to use the normal distribution system. If we go back to Truthmosis’ discussion of the topic we can see that his scale more or less matches the normal distribution, as does his picture scale.

So, I guess I should start using the normal distribution scale to match up with others around here.

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Anyway, back to the women who started this discussion:

As we can see, she’d probably be plain, not fat. So, my initial impression of her as a 7 on the decile system was correct. If we used a normal distribution she’d be a 6.

Someone ranking her a 5 is implying she’s ugly, which I do not think this picture supports. Whoever ranked her as a 3 is just dead wrong; she’s neither obese nor deformedly ugly.

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A few last notes:

I knew the obesity crisis was bad, but I was surprised that 64% of adult women and 74% of adult men are overweight. That’s just plain nuts.

Also, only about 40% of women would be attractive enough to be worth even considering marrying (not even including other factors). So, if you’re looking to marry, make sure you’re in the top 40% of men or you’re going to end up with someone fat or unattractive.

I hope you’ve enjoyed my spergy little analysis.

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Schroedinger’s Rapist

I came across this old post on two major studies of self-reported rape. When asked about having sex with someone against their will (rape by a less off-putting name) about 6% of men said they had (or had attempted to), about 4% of men had said they had repeatedly. In other words, about 4-6% of men are rapists. This is a little more than I thought it would be (my guess would have been about 2-4%). Of these rapes, 70% of them involved intoxication. (I would have thought this to be much lower, in the 30-40% range).

I think the first survey (Lisak and Millar) at this link is probably more accurate. The narrowness and exactness of the questions prevents the problem of subjective opinions on rape. The self-identification aspect of it prevents the problem of false accusations of subjective feelings on the point of the accuser getting in the way (‘I may have consented, but it felt like rape’). The non-judgmental tone (ie. the word rape is not used) would likely limit underestimation. All in all a good survey.

The second (McWhorter) survey I would think would be less accurate and overestimate incidences of rape. It is done of naval recruits and, not to knock the military, but men in the military are likely more highly aggressive with higher testosterone than the average man, and therefore more likely to engage in aggressive sex, of which rape would be one type. But other than a higher overall rape incidence (13% as compared to 6%) the breakdown of rape is similar.

Here’s some thoughts:

From this we can tell that most rapes are the products of a small minority (4%) of men. Only a third of rapists rape once, then never do so again. It would seem from this that anti-rape education might be effective against the 2%, but the majority of rapists and the majority of rapes are committed by a 4% who are committed to their raping. It seems unlikely that anti-rape education would be effective against the repeat offenders, as their actions do not seem to be ‘mistakes’ or a lack of understanding of consent, but rather purposeful actions.

The vast majority of men (94%) have not raped at all. Most men are good in this respect. Any anti-rape campaign that may seem to implicate all or most men as rapists or potential rapists would likely decrease their sympathy for those who are actually raped. As well, a high incidence of false rape accusations may do the same.

Given that a third of men have rape fantasies and only 4-6% rape, we then know that about 15% of men act on their fantasies, which means 85% of men who are inclined to rape do not do so. The large majority of men have base control over their primal urges and know that rape is wrong.

It would seem prudent for women to be somewhat cautious of men, particularity in certain contexts, but a generalized fear would be coutnerproductive.

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As noted earlier, 70% of rapes involve the victim being intoxicated. Any reasonable person who actually wants to stop rapes would advise women not to become intoxicated to the point they are incapable of resistence.

Given that the large majority of rapes could be prevented by women simply not getting shitfaced, it would seem prudent for women to simply avoid drunkenness.

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About 8-10% of rape reports are false, (although, it might be higher). Compare that to the 4-6% (possibly up to 13%) of men who are rapists.

Any women who would apply the Schroedinger’s Rapist heuristic should also be in favour of applying caution to the immediate accepting of rape accusation given that any particular rape accusation is more likely to be false than any particular man is to be rapist.

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Given these thoughts, if I were to attempt a campaign to stop rape I would focus on two things: alcohol and repeat offenders.

The majority of rapists are serial rapists and the majority of rapes involve alcohol. What needs to happen is that serial rapists need to be reported and punished. If serial rapists are either removed from society or discouraged through punishment the large majority of rapes will be prevented.

Second, any campaign needs to emphasize to women to stop drinking to excess. Any other advice concerning self-defence, avoiding certain clothes, being confident, etc. all pales in comparison to simply not getting drunk. Any prudent women looking to avoid rape will avoid drunkenness.

I would also strongly condemn and discourage false rape reports. False reports add to the signal noise making it harder to find and deal with serial rapists who are committing most rapes. If people start believing that many rape reports are fake, they will be less inclined to accept accusations against a serial rapist.

Things I would avoid:

Advice to men such as “don’t rape” or discussions of the the finer points of consent. The advice of “don’t rape” is silly, as these serial rapists doing the majority of raping likely know what they are doing is wrong. Telling them what they already know is not going to help. As well, telling the 94% of men who don’t rape condescending advice of “don’t rape” (ie. assuming they are going to rape unless told not to) will likely make them less sympathetic and backfire.

Anything that looks like it is accusatory of the general male populace rather than rapists themselves. Again 94% of men don’t rape and won’t look kindly upon being lumped in with the 6% who do and the 4% who repeatedly do. You want the 94% to try to distinguish themselves as much as possible from the 4%. The worst thing for a campaign is if the 94% begin sympathizing or identifying with the 4% in any way.

Those are my thoughts on this issue for now.


Guest Post from Europia: The Importance of Fathers

Below is a guest post contributed by someone who wishes to remain anonymous. Remember, If you have something you want to say that is in line with the blog’s purpose and topics, feel free to send it to me.

3. I just thought that these figures MIGHT interest you, even though some of them might be a generation old.

3.01: I just thought that you would find the following FACTS from Social History of some interest. The 43% of U.S. children who live without their father (U.S. Bureau of the Census) account for the following:
3.02: 60% of America’s rapists came from fatherless homes. Source: “Life without Father,” copyright 1996 by David Popenoe. Reprinted by permission of the Free Press, an imprint of Simon & Schuster Inc.
3.03: 63% of youth suicides are from fatherless homes (U.S. Department of Health and Human Services and U.S. Bureau of the Census).
3.04: 70% of long-term prison inmates as opposed to youths in prison, are fatherless. Source: “Life without Father,” copyright 1996 by David Popenoe. Reprinted by permission of the Free Press, an imprint of Simon & Schuster Inc.
3.05: 70% of youths in state-operated institutions come from fatherless homes (U.S. Dept. of Justice, Sept. 1988).
3.06: 71% of pregnant teenagers lack a father (U.S. Department of Health and Human Services press release, March 26, 1999).
3.07: 71% of all high school dropouts come from fatherless homes (National Principals Association Report on the State of High Schools).
3.08: 72% of adolescent murderers grew up without a father. Source: “Life without Father,” copyright 1996 by David Popenoe. Reprinted by permission of the Free Press, an imprint of Simon & Schuster Inc.
3.09: 75% of all adolescent patients in chemical abuse centers come from fatherless homes (Rainbows for All God’s Children).
3.10: 80% of rapists with anger problems come from fatherless homes (Justice & Behavior, Vol 14, p. 403-26).
3.11: 80% of adolescents in psychiatric hospitals come from broken homes. US Dept. Of Health & Human Services (1988
3.12: 85% of all youths in prison as opposed to long-term prisoners come from fatherless homes (Fulton Co. Georgia, Texas Department of Corrections, 1992).
3.13: 85% of all children who show behavior disorders come from fatherless homes (Center for Disease Control).
3.14: 90% of all homeless and runaway children are from fatherless homes (U.S. Bureau of the Census).

4.01: Regarding “Deadbeat Dads” you may find these figures of some interest.
4.02: 90.2% of fathers with joint custody pay the support due.
4.03: 79.1% of fathers with visitation privileges pay the support due.
4.04: 44.5% of fathers with no visitation pay the support due.
4.05; 37.9% of fathers are denied any visitation.
4.06: 66% of all support not paid by non-custodial fathers is due to the inability to pay.
4.07: In 1992 the General Accounting Office (GAO) found 14% of fathers who owe back child support are dead.
Source 4.02 – 4.06 inclusive [1988 Census “Child Support and Alimony: 1989 Series” P-60, No. 173 p.6-7, and “U.S. General Accounting Office Report” GAO/HRD-92-39FS January 1992]

5.01: 61% of all child abuse is committed by biological mothers (Department of Health and Human Services Report on Nationwide Child Abuse).
5.02: Rates of serious abuse are lowest in intact families; six times higher in stepfamilies; 20 times higher in cohabitating biological parent families and 33 times higher when the mother is cohabitating with a boyfriend who is not the father. (UK research).
5.03: 70.8% of children killed by one parent are killed by their mothers! 206 (National Child Abuse and Neglect Data System)
5.04: 70.6% of children abused by one parent are abused by their mothers! (U.S. Dept. of Health and Human Services Child Maltreatment reports from 2001-2006

6.01: Child Murders
6. 02: Killed by Mothers 1,100
6.03: Killed by Live-In Boyfriends 513
6.04: Killed by Stepfathers 250
6.05: Killed by Biological Fathers 137
Source of 6.02 – 6.05 inclusive, The Heritage Foundation report “The Child Abuse Crisis: The Disintegration of Marriage, Family, and the American Community,” May 15, 1997.


Lightning Round – 2012/10/10

A salute to conventional wisdom.

Destroying our kids, one drug at a time.
Related: John Dewey is one of the worst Americans ever.

If she’s had sex before marriage, she’s probably had better sex before she married you.
Related: Ruined by 5 minutes of alpha.

Debasing marriage.
Related: Peter Pan Manboys.
Related: Mark Minter on marriage. Nihilism in action.
Related: The importance of marriage. Part 2.

Feminist responds to Aurini. Can’t handle red pill; calls him a monster;.
Aurini responds.

The Bible: the original Red Pill.

Some brides are just disgusting.

Most women aren’t worth chivalry.

No dating relationship should last 9 years.

Game Theory: The Axioms of Game.

The misandry bubble has popped. The anti-feminism bubble is beginning.

Boomers and the War on the Young.

SAT Data: Boys score better, even though girls do better in school.

The manosphere is for men.

The good guys win one.

Female doubts about a marriage lead to divorce (men’s don’t).

Science: Slowly destroying egalitarianism brick by brick.

Better strength than smarts.

Frost contemplates being back home.

As I’ve written before: child care is not economical.

Cool. I hate the phone, but I hate texting even more.

Why liberals are ugly redux. The original.

Society requires old men to be dangerous.

The decline occurs because society is corrupt at every level.

Liberal economics. We trade “leadership” for stuff.

Estonia: Austerity works. Screw you Krugman.
So did Reagenomics. Screw Keynesianism.

Producer tells the truth. Leftists freak out.

Alternatives to tough luck for libertarians.

Socialism in action. Good food banned in schools.

I hate the phrase “correlation doesn’t equal causation“. It is almost always used as an intellectual cop-out by people who don’t understand it.

The miracle of photoshop.

Hehe… Tolerant leftists and dating conservatives.

Striking is for ignoramuses without self-respect.

How it feels to be smart. I’m not quite as smart as the writer, but his observations seem about right.

(H/T: SDA, Maggie’s Farm, Bitter Babe, 3MM, the Captain, Instapundit, Shining Pearls, RWCAG)


Violent Crime and Gun Ownership: Stats

Number of guns: 260 million

Number of gun owners: 80 million

Number of Homicides using firearms: 8,775

Number of white persons: 241,747,756

Number of homicides by white persons: 4,849

Number of black persons: 40,445,666

Number of homicides by black persons: 5,770

Number of males: 151,781,326

Number of homicides by males: 9,972

Number of females: 156,964,212

Number of homicides by females: 1,075

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Odds of any particular gun being used to murder you: 0.0034%

Odds pf any particular gun owner murdering you: 0.0110%

Odds of any particular white person murdering you: 0.0020%

Odds of any particular black person murdering you: 0.0142%

Odds of any particular male murdering you: 0.0066%

Odds of any particular female murdering you: 0.0007%

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Draw your own conclusions.

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(All numbers for in the US and, where applicable, per year: most numbers for 2010)