Lies, Computer Models, and Government Subsidies

Updating Mark Twain's famous opinion that "Lies, Damned Lies, and Statistics" were three types of untruths, with "statistics" being the worst, I presented "Lies, Damned Lies, Computer Models, and Government Subsidies" to an astute audience at the Science-Technology Club at The Villages, FL, today. You may download the Powerpoint Show HERE.

COMPUTER MODELS ARE VERY USEFUL (BUT MAY BE MISUSED)

I generally love Computer Models, having produced several useful ones myself *. However, when it comes to the misuse of Climate Models to justify spending hard-earned taxpayer money for unworthy projects, my love has its limits.

I showed the attentive and interactive audience how I was able to model the latest NASA-GISS Global Land-Ocean Temperature Index using two sinusoids and one exponential. (See the graphic, above. The bright red line is the 5-Year Running Mean of the Temperature Anomaly in °C from 1880 through 2014. The blue and red sinusoids, representing natural cycles, have periods of 33- and 70-years, respectively, and the green exponential represents the increasing levels of "greenhouse" gases. Note how the thick black line, which is the sum of the sinusoids and the exponential, fairly closely matches the NASA-GISS Temperature Anomaly.)

Of course, the easy part of computer modeling is retro-dicting the past. As John von Neumann famously told Enrico Fermi, “With four parameters I can fit an ELEPHANT, and with five I can make him wiggle his trunk.”

The hard part is predicting the future, and I make no claims regarding my simplistic model's ability to do that. However, the IPCC (Intergovernmental Panel on Climate Change) and the rest of the Official Cliimate "Team" do take their models seriously. In the latest IPCC Assessment Report, they continue to predict a catastrophic future based on their failed models.

These models failed to predict the current 15- to 18-year "pause" in Global Warming, despite the increasing -even accelerating- levels of Atmospheric CO2. Furthermore, as Dr. Roy Spencer recently showed, only TWO out of 90 CMIP5 Climate Models, used in the latest IPCC Annual Report, agree with the OBSERVED SURFACE and LOWER TROPOSPHERE TEMPERATURE DATA.  Thus, over 95% of the IPCC models AGREE that, in Spencer's satirical words, "the OBSERVATIONS must be wrong" :^)

Climate Alarmists and Warmists have convinced the US, UK, and many other governments to spend tremendous amounts of taxpayer money to study the problem and to impose costly regulations to curtail human production of "greenhouse" gases.

The problem with attempts to model the Climate is that it is a combination of linear and chaotic elements, and the latter makes it virtually impossible to correctly predict the future beyond a relatively short period. See my PowerPoint Show for how I demonstrated that a chaotic model is very sensitive to initial conditions. Indeed, in my chaos model, a change in initial conditions of less than one part in a million, produced very large changes in longer-term results.

GOVERNMENT SUBSIDIES MAY BE USEFUL (IF NOT POLITICALLY ABUSED)

My talk concluded with a review of how necessary government spending, such as the vast expenditures on military aircraft during WWII and subsequent conflicts, may benefit industry and consumers, such as the commercial aircraft and airline industries. Similarly, the Space Program and Medical Research expenditures are mostly justified by the benefits they have brought to the taxpayers.

However, there is great danger when the government unnecessarily expends large sums and burdens industry and consumers with un-affordable costs for environmental purposes that are "justified" by failed Computer Climate Models.

For example:

Ethanol: The requirement that up to 15% Ethanol, derived from corn, must be blended with gasoline, despite higher costs and reduced MPG, appears to be a politically-motivated subsidy for the agricultural industry and states where corn is a major crop.

Solar Panels: US taxpayers lost $500M when solar-panel producer Solyndra went bankrupt. It appears that political influence was used in 2009 to push through a loan for them to produce solar panels in the US, despite the fact that their cylindrical technology cost several dollars per watt, as compared to flat panels available at less than a dollar per watt. They went bankrupt in 2011, only two years after the loan, and all employees lost their jobs.

There are many other examples, too numerous to mention!

Ira Glickstein

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A Practical View of Bayesian Inference

Bayes Theorem has practical applications. Use it to make real world decisions. [Updated 8 Sep 2013]

Bayes Theorem is not just an obscure artifact of the statistics of probability handed down to us from centuries ago. You can use it now to make decisions that will affect your financial well-being.

A relatively simple Excel-based tool helps you choose the right course of action in the face of uncertain probabilities and inexact test results. It is available for FREE.

[Presented to the Science-Technology Club, The Villages, FL, 9 September 2013. For a copy of my PowerPoint slide show, send an email to ira@techie.com.]

What can the Bayesian Inference Advisor Tool do for You?

I know you are probably not in the oil business (and neither am I), but the best way to understand an abstract concept is to go through a practical example. The example below has to do with oil exploration and drilling, but the Bayesian Inference Advisor can handle any problem where you know:

1) the probability of success if you take action without doing further testing,
2) the cost of further testing and the probability the test results will be reliable,
3) the cost of taking some action,
4) the financial benefit to you if the action is successful, and
5) the compensation you expect for taking the risk.

You can download the Bayesian Inference Advisor at:
https://sites.google.com/site/bigira/stuff-ira-knows/BayesianInferenceAdvisorV3.xls?attredirects=0&d=1

The Bayesian Inference Advisor will compute the most likely financial implications of your actions. What if you proceed without further testing? What if you get a positive test result and proceed? What if you get a negative test result and proceed? Several other examples in very different domains are included later in this posting, but for now, put on your hard hat and let us imagine we are in the oil business!

Psychology of Green Hypocrisy

Psychological studies sometimes come up with strange results.

The UK Guardian reports: "Canadian psychologists Nina Mazar and Chen-Bo Zhong, argue that people who wear what they call the 'halo of green consumerism' are less likely to be kind to others, and more likely to cheat and steal. 'Virtuous acts can license subsequent asocial and unethical behaviours,' they write."

Say what?

Here is a link to the full research report.

Here is a link to a refutation of the study methodology.

The University of Toronto researchers recruited 156 undergrad students and randomly split them into two groups. Each member of the first group had to "buy" up to $25 worth of items at an online store that had mostly green products. Each member of the second group had to "buy" $25 worth of items at a similar online store that had mostly conventional products. Both stores had the same number of products in the same categories and at the same prices.

Would "purchase" of green products make members of group #1 more ethical and altruistic than group #2?

Well, in the next phase, when given an opportunity to share $6 with an anonymous participant, those who had "purchased" in the green store kept more of the money than those who had "purchased" in the conventional store. Thus, conditioning by having done a "good deed" by choosing green products, made participants less altruistic than others who had not been given an opportunity to do that "good deed".

In a later experiment, 90 students were similarly conditioned by having half in group #1, making "purchases" at the green store and half in group #2, at the conventional store.

Then, they were asked to play a computer game for monitary reward where it was easy to lie to increase their rewards. They were also allowed to privately make their own change out of an envelope with money in it, offering an opportunity to steal.

Would "purchase" of green products make members of group #1 more truthful and honest than group #2?

Guess what. The green store participants lied and stole more than the conventional participants. Again, having done a "good deed" licensed the participants to lie and steal! (Even though the participants knew the green and conventional stores were not real.)

The refutation of the study methodology maintains it was flawed because the participants were randomly assigned to the green and conventional groups. Had the experimenters determined which participants were really green consumers and which were not, the results, they say, would have been the reverse.

What do you think?

Ira Glickstein

LIES, DAMNED LIES, AND STATISTICS (Part 6)

STATISTICS OF HEALTH CARE SPENDING
Health Care reform has been a hot topic where statistics have been used to: Abuse anecdotal math to falsify the truth and truthify falsehood.

(This is the sixth of the series on misuse of statistics. For the earlier postings, click: 1-Going to St. Ives, 2-Playing Percentages, 3-Correlation and Causation, 4-Fun with the Normal Curve, 5-Global Warming.)

In a December 2009 posting, I pointed out that the map of per capita Medicare spending by county in the US looked a lot like the political division between the "Blue Counties" (Democrats, L-Minds) and the "Red Counties" (Republicans, C-Minds).

Since counties are so numerous and therefore confusing, I used Congressional Budget Office 2004 statistics of per capita Medicare spending on a statewide basis to show that the top five Highest Spending States tended to be Blue States and the top five Lowest Spending States tended to be Red States.

(My stated purpose -agenda if you like- was to indicate that Liberals consume an outsized share of the common pot of health care resources, as compared to Conservatives who take a smaller piece of the pie per capita.)

The Central Limit Theorem (CLT)

[From Stu. Images added by Ira from this source.]

OK,OK Ira, you have guilted me into action and so I will share something that has been bothering me lately. As many of us may know, the CLT is, next to the Law of Large Numbers, the most important principle in Statistics and is used to justify many a research study. So, I am thinking it behooves us all to try to understand this CLT so we can become more discerning citizens, n'est pas?

Here is the way I understand it. Given a random variable, X with mean mu and standard deviation sigma (X may or may not be normally distributed). Now we do the following m times: We draw n samples from X and compute the mean X-Bar giving us m X-Bars which will have their own particular probability distribution, PD. Finally the CLT promises that as m and n approach infinity, PD will approach a Normal distribution with mean mu (the mean of our original random variable X) and a standard deviation of sigma divided by the square root of n (the sigma of X and the n of the n samples). Pretty amazing actually. Please correct me if my understanding of this is incorrect as I'm going from memory here.

Now here's the problem that is bothering me. Say a research study is done where the researcher does not know the actual probability distribution so he or she can use the CLT to draw inferences about the population but precisely how? From what I understand they want to use as large an n as possible but surely do not use a large m (repeated sampling). And while I understand that once you have a normal/Gaussian probability distribution, it's easy to compute deviations from the mean and confidence intervals, just exactly what is the procedure used. Can anyone give me a useful easy-to-understand example?

Bewitched, bothered and bewildered,
Stu

Madoff: Social Security for Fat Cats Goes Bust

HOW MADOFF DID IT AND WHO BENEFITTED

Uncle Bernie Madoff allegedly had a great social security program for the financially well-off. While they were earning the big bucks they'd give millions to him. He'd report growth on these investments of twelve percent a year or more. Every reporting period he'd check which stocks and options went up a lot (or down a lot) and pretend to have bought puts and calls and shares of them. Even a fool can pick the winner after the game! But Madoff was no fool. He had his own brokerage firm and could easily create the trade tickets, after the fact, to document the gains.

If you saw CBS 60 Minutes last evening, you have a better idea of what he did and how.

Madoff and his family, and the network of financial advisors and feeder funds who earned big fees steering eager clients and funneling investment money to him, spent their share of the money on the good life.

Meanwhile, his clients, when they were no longer earning the big bucks, were able to draw whatever money they needed out of their bloated accounts to continue their posh life styles. On their death beds they'd advise their wives (and girlfriends :^) to leave their money with Madoff and draw millions a year for the rest of their lives. The inflow of cash from new investors was more than enough to pay off those who cashed out. Many of the early clients probably received far more from Madoff's social security program than they ever put in. Much more than had they put their money into CDs.

Of course it all came to an end with the recent market crash. Lots of his clients need to cash out and few new ones had the cash to put in. OOPS!

WHAT DOES THIS REMIND YOU OF?

Uncle Sam has a similar program for us ordinary folks. Social Security was great for my grandfather who was near the end of his working career when it started. The rates were low because there were over 150 workers putting in for each beneficiary taking out. He told me he put a few hundred bucks in and collected thousands by the time he passed away at a ripe old age.

My mom and dad also did well. He worked for the post office and, at the time, did not pay into Social Security because the government had their own pension plan. After he retired at an early age and started drawing his post office pension, my dad took a job in the private sector and put enough money into Social Security to qualify as a "double-dipper" when he finally retired for good. My mom worked most of her life "off the books" in my grandma's knitting shop. Then, in her last working years she took a job and paid into the Social Security system to qualify for benefits when she retired for good. During the few years my mom and dad were paying in, the rates were low because there were over ten workers for each beneficiary. Mom and Dad lived long lives and took way more out of Social Security than they ever put in.

My wife and I paid into Social Security our whole working lives. During that period there were a bit over three workers paying in for each person collecting, so the rates paid by us and our employers out of our real earnings were high. We've put over $300,000 into the system. Had that money been invested in CDs they'd be worth about three times as much now. Had we used that money to directly pay our grandparents and parents Social Security benefits the CDs would have still been worth around twice what we and our employers put in.

But, the government took all that money from us and our employers and almost immediately paid it out to beneficiaries. It is unlikely my wife and I will ever break even with Social Security.

Our daughters and sons-in-law, and their employers, have been paying in at high rates since they've been employed. During that time the rates have gone up as has the age to qualify for payouts. I do not think they will ever get their money out. It is a sucker's investment and I doubt anybody in his or her right mind would pay into it if it was voluntary.

WHY DIDN'T THE SEC CATCH MADOFF BEFORE HIS SCHEME WENT BUST?

On the 60 Minutes program last evening, you saw Harry Markopolis recount how he had alerted the SEC multiple times. While working for a competitor firm to Madoff's, he said he figured out Madoff must be a fraud in "five minutes". It only took him a few hours using math models to confirm his hunch. Of course, now -after the game and we know Madoff was a fraud- it is so obvious!

According to the Markopolis 2005 Statement to the SEC, he had alerted the SEC early as 1999 (on Clinton's watch). On the CBS progam, he mentioned alerting them several times between 2000 and 2007 (on Bush's watch). I am no financial wizard, but, as I read the Markopolis statement, all he had was something called Mosaic Theory. In his SEC statement he raises a bunch of what he calls "Red Flags". I read through the Red Flags and none provide any evidence beyond statistical math model generalizations and speculation. Markopolis says (unnamed) senior managers and heads of Wall Street equity derivative trading desks privately agreed with him that Madoff was a fraud. However, Markopolis admits he has no "whistleblower or insider" contacts who would know exactly what was going on or have any documentary proof. He also gave his information to a reporter for the Wall Street Journal but his proof was not good enough for them either.

Of course, in retrospect, he turned out to be totally correct. Had someone at the SEC or WSJ followed through, they could have become famous and saved a lot of people lots of money, but they did not.

LET'S INVESTIGATE !

Imagine you are a civil service lawyer or accountant working for the SEC and you are put on the Madoff case after several tips come in that "it is too good to be true". (Remember you are a civil servant -your main job is to show up every working day and not make waves- if you were highly competent and aggressive you'd probably be working for more bucks in the private sector :^)

OK, so you go to investigate. You are welcomed into opulent headquarters and shown reams of (fake) data that looks good because Madoff has his own brokerage firm. You are shown audit reports from Friehling & Horowitz, a small CPA firm -not one of the big ones- but they have a good record, and, after all Enron was audited by big Arthur Anderson and look what happened with them! Madoff tells you he makes money when the market goes up and when it goes down, but he can't make money when it stays flat. Sounds like a good story.

You are shown a list of big time financial advisors, feeder funds and clients from the New York City area, Palm Beach Florida, Greenwich Connecticut; and all over Europe who vouch for Madoff. None of Madoff's clients have complained -not a single one- they are all deleriously happy with their investment results.

Look, there are nine accounts held by men named "Ira":

IRA LEE SORKIN 10 VANAD DRIVE EAST HILLS, NY 11576
IRA M RUBENSTEIN CPA MANAGING PARTNER ERE 5TH FL 440 PARK AVENUE SOUTH NEW YORK, NY 10016 08012
IRA PITTELMAN 1385 YORK AVENUE APT 29-B NEW YORK, NY 10021
IRA S SKLADER 6600 LYNDALE AVENUE SO #1307 RICHFIELD, MN 55423
IRA S SKLADER 8143 RHODE ISLAND CIRCLE BLOOMINGTON, MN 55438 01152
IRA SCHWARTZ C/O HAROLD SCHWARTZ 989 SIXTH AVENUE 7TH FL NEW YORK, NY 10018
IRA SCHY OR ROSE SCHY J/T WROS GUILDFORD E #3073 BOCA RATON, FL 33434
IRA SIFF 211 EAST 11TH STREET APT 9 NEW YORK, NY 10003
IRA SKLADER GAIL SKLADER JT WROS 6600 LYNDALE AVENUE SO #1307 RICHFIELD, MN 55423

One client even has a home in The Villages, FL, the retirement community where my wife and I live and that is well known for attracting the best and the brightest:

JOHN FOGELMAN AND ROSALIE FOGELMAN TTEES, JOHN & ROSALIE FOGELMAN RV LV TST 1556 LYMAN WAY THE VILLAGES, FL 32162

Fortunately, Uncle Bernie has not Madoff with any of my money, though I wish I had enough to have qualified!

And, what a list of clients! The cream of the crop of the rich, many are famous, and most of them are Jewish. Markopolis said Madoff was running an "affinity scam", preying on his own kind.(Archie Bunker famously said "Them people really know how to handle money." Well, he was wrong about some of us, at least :^)

You show the information about Madoff and the client list to your superior at the SEC. He checks and Madoff is a big time political contributor, as are many of his clients. Most to Democrats but a bit to Republicans as well. If we go after him will it look like we are on a political mission? And, he is well respected among business associates. For God's sake, he was formerly Chairman of the NASDAQ. The National Association of Securities Dealers Automated Quotations is an American stock exchange, the largest electronic screen-based equity securities trading market in the United States.

So, what would you do if you were at the SEC? (Or the WSJ?) I think I'd put the tips down to jealous competitors and apply manpower to more pressing matters. That is exactly what they did.

BOTTOM LINE

The government cannot do much right. I'm not sure I'd want to live in a country where the government had access to the internal data of every company that might be planning or executing a scheme. Even if they had access to all that information, I don't think the kind of people who tend to work as civil servants would recognize such a scheme even if it fell on their heads.

So, Madoff and probably some of his relatives and/or others who can be proven to have known they were running a Ponzi scheme will spend some time in jail and lose most of their wealth. Clients who lost big bucks in the scheme will sue the financial advisers and feeder fund managers for lack of due dilligence, gross negligence, failure of proper risk management, and so on, and some of them will probably have to pay up, at least a bit.

I have trouble feeling sorry for most of the rich victim clients and financial advisers and feeder fund managers. My sympathy is with the poor folks who depended upon the mainly Jewish charities that invested heavily with Madoff. Those charities have lost much of their money and their poor clients will have to do without, through no fault of their own.

Ira Glickstein

L/C Good Vibes vs Good Deeds

Do liberals earn less money that conservatives? Nope - liberal's family income averages 6 percent MORE than conservative families.

Do conservatives give less of their income to charity than liberals? Nope again - on average, conservative headed households give 30% MORE!

This surprising (to me) statistic throws new light on our ongoing L/C discussion. For the details, see Washington Post columnist George Will's recent column at: http://www.washingtonpost.com/wp-dyn/content/article/2008/03/26/AR2008032602916.html

Perhaps "compasionate conservative" is not an oxymoron? Perhaps liberals like to talk about helping the poor and downtrodden, but when they take action it is by taking higher taxes from the rich and giving it to the poor while keeping a bit for themselves in the form of bigger government with more social services jobs for them and more votes "bought" with government help programs paid for by us taxpayers?

The data in Will's column are from a Syracuse University professor's book and include the following:

• Conservatives give more blood and donate more time.
• Do you REJECT the idea that "government has a responsibility to reduce income inequality"? If so, you belong to a group that give FOUR times more than those who accept that statement!
• People who live in the reddest states give nearly twice the percentage of income to charity as those in the bluest states.

Also mentioned is the strong correlation of altruism with being associated with an organized religion. Perhaps religious belief is the "The God Delusion" (Dawkins) http://tvpclub.blogspot.com/2008/03/god-delusion.html and "god is not GREAT" (Hitchens) http://tvpclub.blogspot.com/2007/12/god-is-not-great.html, but that type of faith leads to actual, personal giving while the opposite leads to talking about it and getting "good vibes."

Ira Glickstein

LIES, DAMNED LIES, AND STATISTICS (Part 4)

All about the abuse of anecdotal math to falsify the truth and truthify falsehood.

This is the fourth part of my "presentation" on the topic of "Lies, ..." Click for Part 1, Part 2, and Part 3.

This part is about the "Normal Curve".

The height of young American women ranges from about 4' 9" to 6'. For young men it is 5' 2" to 6' 5". That's a difference of about five inches -- less than ten percent.

Therefore, in basketball and other sports where height is critical, you'd expect about ten percent fewer women than men. Right?

Anything less would be proof of discrimination against women. Right?

WRONG !!!

Actually, if you had a cut-off of six feet, over 100 men would qualify for every woman who qualified! Even if you had a cut-off of 5' 7", which is the average height of the population of young men and women combined, you'd find over five men for every woman who qualified.

WHAT IS GOING ON HERE?

Why are Our Expectations Wrong?

Glad you asked!

You have probably heard of the "Normal Curve" or the "Bell-Shaped Curve" and if you stay tuned for a bit you will understand what that is and why it is important. I promise to keep the math to a minimum and the understanding to a maximum.

The curve is called "Normal" because, when you make lots of measurements, such as the heights of a bunch of random people, you normally get a "Bell-Shaped Curve"!

Most of the measurements will be near the average value and you will get fewer and fewer as you go further away from the average.




[Click figure for larger view] The figure shows the Normal curve for the heights of young women (in red) and young men (in blue). Notice how each group of measurements resembles the shape of a bell?

Please look at the red bars that represent measurements of a thousand young women. Nearly all of them are between 57" and 72", a range of fifteen-inches. No more than five out of a thousand will be below or above that range. If you divide that range into six equal increments of two and a half inches each, about two-thirds of them will be in the two increments closest to the middle.

As indicated in the figure, in ordinary English, we would say women in that range are of "average" height.

On either side of the "average" are increments for "short" and "tall". Out of 1000, there will be about 136 "short" women and 136 "tall" women.

On either side of "short" and "tall" are "very short" and "very tall". Out of a 1000, there will only be about 21 "very short" and 21 "very tall".

The handful of women who fall outside the range would be called "extremely tall" and "extremely short".

The same situation prevails for young men, shown in blue. But note: the measurement results are shifted two increments to the right. A woman we'd call "tall" or "very tall" would be "average" if she were a man. Similarly, a man we'd call "short" or "very short" would be "average" if he were a woman.

None of the above is controversial. These are simply the facts that can be verified by anyone who would like to do the measurements.

Mathematical Terms (I'll keep this very short :^)

Mathematicians call the area that contains 68.3% of the measurements the "plus or minus one standard deviation" range. Since standard deviation is usually represented by the Greek letter “sigma”, this is called the “one-sigma” range.

A mathematician would analyze the height measurements and calculate the standard deviation as 2.5". He or she would note that the average for males is 5" above that for females and conclude that males are two standard deviations taller than females.

Please don't worry about the math terms "standard deviation" and “sigma" too much. These terms are just a fancy way of saying where to expect 68% of the measurements to be.

Representation of Women in Sports

For basketball, height is obviously a critical factor. If high schools and colleges insisted on having unisex teams, we'd find boys and young men outnumbering girls and young women by one-hundred to one! That would not be fair to girls and young women who want to play sports. That is why it makes total sense to separate basketball teams by gender.

Since height often correlates to strength and speed and other factors that are important in baseball, football, soccer and many other sports, it also makes sense to separate those sports by gender. In fact, only a small number of sports (gymnastics comes to mind) favor participants who tend to be shorter.

Bottom Line

1) In high school, college, and other amateur play, I favor separation by gender in the sports where males have a significant advantage. I would make an exception for the few girls and women who could qualify and allow them to join the male division if they wanted to.

2) For professional sports, I would make it illegal to exclude women from the highest level in any given sport. There are women who qualify, and, however few their number, it is unfair to exclude them. On the other hand, for the lower levels of professional sports, I would allow separation by gender to give highly qualified women a fair chance to play at their level.

BUT WHAT ABOUT INTELLIGENCE???

OOPS - here is where we may get "politically incorrect".

Now that you understand all about the Normal curve and standard deviation and so on, let us apply our newfound knowledge to a different domain.

Lots of well-meaning people are misinformed about standardized tests, particularly those that are said to measure "intelligence".

I'll be the first to admit that some college graduates with advanced degrees don't have the intelligence to "rub two sticks together to save their lives". Some with the highest academic honors could not survive more than a few days in the woods or on the streets of a big city.

Some PhDs are at a total loss when it comes to doing carpentry or plumbing or fixing a TV set or PC or a car. They cannot grow fruits and vegetables and would be a total failure at "animal husbandry" (whatever that is :^) Some of them have no social intelligence at all and cannot sing on key or play a musical instrument. I would not want to "have a beer" with many of them.

The standardized so-called "Intelligence Quotient" (IQ) test does not measure any of the above talents.

However, IQ tests do a damn good job of evaluating "normal" people as to their ACADEMIC INTELLIGENCE.

People with high IQs generally excel in high school and college. They also excel at jobs that require lots of reading and writing and designing and science and math and so on.

People with low IQs generally do not do well in school and they find employment in fields that do not require academic-type talents.

Design of IQ Tests

IQ tests are designed to yield a score of 100 for the average person and to have a standard deviation of fifteen points. If you give IQ tests to a thousand people (in their native languages), all but a handful will fall between 55 and 145.

Out of a thousand people, about 683 will have IQs between 85 and 115, and will be said to have "average" intelligence. About 136 will be "high" and 136 "low". About 21 will be "very high" and 21 "very low".

A handful will be out of the range. People with IQs above 145 are considered "extremely intelligent".

In some jurisdictions, those below 70, with "very low" or "extremely low" intelligence, are exempted from things like the death penalty because their intelligence is so low they cannot be considered moral agents. A considerable portion of the prison population falls in the range of 80 and below.

Here is the Politically Incorrect Part

What if there was an ethnic or racial group that had an average IQ ten percent above or below 100? Say members of group "Beta" have an average IQ of 90 and members of group "Alpha" have an average of 110? (Actually, there are groups like that, or close to that. However, political correctness forbids me from mentioning their ethnic and/or racial descriptions.)

If the IQ difference between Alpha and Beta was only twenty percent, would you expect the Alpha group to have only twenty percent higher representation among professions that require high academic intelligence? Would you expect Alpha to have only twenty percent more scientists and engineers and accountants and so on? Would you expect Alpha to have only twenty percent more PhDs?

If you did you would be WRONG.

If the standard deviation for IQ is fifteen points, and the Alpha group is twenty points above the Beta group, that is a difference of over one standard deviation.

For example, if a Nobel Prize winner had to be "very intelligent" or "extremely intelligent" in the top two increments, there would be over ten people from the Alpha group for every one from the Beta group.

If you had to have an above-average IQ (100 or more) people in the Alpha group would outnumber those in the Beta group by three to one!


Bottom Line:

If members of some ethnic and racial groups are "over-represented" and other groups "under-represented" in professions requiring higher academic intelligence, that does not necessarily imply discrimination or favoritism.

If one group has an average IQ of 110 or more, you would expect them to be "over-represented" by at least three-to-one over the average American.

If another group has an average IQ of 90, you would expect them to be "under-represented" by a factor of three-to-one or more below the average American.


BUT PLEASE NOTE:

There is a great deal of overlap.

A "tall" woman is taller than 60% of all men and an "extremely tall" woman is taller than 90% of all the men.

A "very intelligent" member of the Beta group is smarter than 60% of all members of the Alpha group and an "extremely intelligent" member of the Beta group is smarter than 90% of all the members of the Alpha group.

Do not judge a person by his or her group membership!

Ira Glickstein

The above is the fourth part of my "presentation" on the topic of "Lies, ..." Click for Part 1, Part 2, and Part 3.