This post is a simple back of the envelope calculation based on Davide Piffer's paper as discussed in my earlier post.
First a quick reminder about opinion polls and sampling.
Opinion Polls
We assume a large population of interest and we sample n individuals (often 1,000) with a yes-no question. Something like "Are you going to vote for the Labour Party in the forthcoming election?" We want to know how likely it is that the population as a whole votes in the same proportions as found in our survey. Suppose p is the fraction of the sample-population who tell us they will vote yes (example: 0.32).
This is just the same as throwing a biased coin (Heads with probability 0.32) a thousand times and seeing how many Heads we actually get. Clearly on average we'll get 320 Heads [the mean of our sample is np]. Of more interest, however, is the standard deviation of the mean if we took sample after sample (or coin-throwing exercise after coin-throwing exercise). We would like to know the upper and lower bounds of 'yes' respondents we would get in, say, 95% of the samples we took, corresponding to +/- 1.96 standard deviations. We can be pretty confident that those bounds would play out in real life (nineteen times out of 20).
The standard deviation of a binomial distribution, which is what we have here, is √(npq) where n is the size of the sample (example, 1,000), p is the probability of the 'yes' outcome (example: 0.32) and q is the probability of the 'no' outcome (0.68 = 1-p).
The 95% confidence interval around the mean np is +/- 1.96 standard deviations - which we approximate here to 2. We also approximate p and q to 0.5 as this is the largest value of √(pq).
Plugging the numbers in, we get the 95% confidence interval as: 2 times √(0.5 x 0.5 x n) = √n.
In our running example with 1,000 people sampled (√1000 equals around 32), this tells us that the interval 320 +/- 32 will contain the number of 'yes' answers we'll get 95% of the time. We sometime prefer to have the results as a proportion, usually written as a percentage, in which case we divide everything by n.
The mean number of 'yes' voters here is 320/1,000 = np/n = p (0.32 or 32%).
The 95% confidence interval here is 32/1,000 = √n/n = 1/√n (usually described as +/- 3%).
Note that if we had sampled just 100 voters, we would have a 95% confidence interval of +/- 1/√100 = +/- 10%. We're already losing quite a bit of predictive power.
Asking just 10 people, the 95% confidence interval is 1/√10 = 0.32 = approx. 30%. So the three people who said they'd vote 'yes' .. in multiple surveys that number could dip as low as zero and as high as six. Pretty much worthless in forecasting the election.
---
To apply this to IQ I'm going to use the data in Davide Piffer's paper, as discussed in my earlier post - to which you may need to refer.
Looking at my own results I had 16 alleles to play with, of which 7 were 'good for intelligence'. So this is an opinion poll where I was able to survey only 16 people. Duh!
My computed allele frequency was 44% against a European average of 35.5% so I'm 8.5 percentage points up from the average.
Looking at the Chinese/Japanese figures we see an allele frequency score of 39.1% (a difference of 3.6% from the European mean) which corresponds to an IQ difference of 5 IQ points from Europeans. I'm going to assume a linear relation - an additive model.
To convert a difference of mean allele frequency to IQ difference we multiple by 5/3.6 = 1.4. So the estimate of my IQ is 8.5 * 1.4 = 12 points above the European average of 100. In my incorrigible vanity I'd like to believe that 112 is rather on the low side! What is the 95% confidence interval for this calculation?
Since n = 16, and following the path described above, the 95% confidence interval is +/- 1/√16 = 25%.
That's the allele frequency limits so my true allele frequency (of those hundreds or thousands of 'good alleles driving IQ') is probably in the range 44% +/- 25% or [19%, 69%]. To change these limits into IQ scores multiply the confidence interval of +/-25% by 1.4 giving +/- 35 IQ points
We may be 95% confident that my IQ is in the range [77, 147].
So I guess we can be 95% confident that I'm neither extremely educationally subnormal nor Albert Einstein!
The take-home message is that we need hundreds of alleles to give us a big enough sample to get the error bounds down. The concordance of twins brought up together for IQ is around 0.86 so non-genetic factors will still prevent us getting all the way.
BTW we're just a few years from getting to that 'hundreds of IQ-affecting alleles' point, so although this is a fun exercise, reality will be along soon enough.
Showing posts with label Opinion Poll. Show all posts
Showing posts with label Opinion Poll. Show all posts
Friday, November 07, 2014
Saturday, August 20, 2011
Ship of Fools?
Part 1
Did you read the SF story where a future population is oppressed by ... well, an evil oppressor, and the time-travelling hero hands the Resistance the ultimate secret weapon, the U.S. Constitution? It may be one of the classic SF clichés but it speaks to a bedrock conviction of American culture: Democracy is Wonderful.
In truth it would be wonderful if the average voter wasn’t so incredibly ignorant:
How on earth do we get sensible representatives, policies and decisions from this ocean of baleful ignorance? We know that most voters don’t understand micro- or macroeconomics and fail to appreciate the subtleties of foreign policy. Given the likely effects of their one vote, it’s even been argued that it’s rational for them not to invest much time in preparing for an act of so little practical consequence.
Thankfully, it might not be quite as bad as it appears.
An example: suppose the 160 million registered voters in North America have to vote for candidates Ms Right or Mr Wrong. If we have 160 million totally ignorant citizens, then Ms R and Mr W are each going to get around 80 million each and it’s a toss-up as to who will win. We can model this outcome by simply imagining that each voter has a coin and just flips it: Heads for Ms R and Tails for Mr W.
If we imagine running that vote over and over again, then 99% of the time the total number of votes cast for each candidate would each be within 17,000 of the magic balancing 80 million votes. That is, when we counted the number of votes for Ms Right (or Mr Wrong) then with 99% probability the number of votes she or he would receive would lie between 80 million minus 17,000 and 80 million + 17,000. This is the magic of statistics* and also a possible answer to our dilemma.
Suppose that there are 20,000 well-educated and politically-engaged people in the electorate (just 0.0125% of the 160 million voting population).
Suppose that Ms Right’s political supporters successfully persuade these 20,000 to vote the Right way. A result! Democracy has successfully delivered the goods, despite an electorate which scarcely deserves such good fortune. So Democracy works even with overwhelming public apathy and ignorance if there is tiny crowd of political sophisticates who take the trouble to research the issues and vote the right way.
But be cautious: is this a model which describes the actual political world we see around us? Sadly not: in point of fact the true situation is even worse than widespread and systemic ignorance. The population-at-large in fact exhibits perverse and self-defeating bias: the people systematically vote for candidates and policies which act against their own self-interests.
How and why this happens will be discussed in part 2 of this article below.
---
* Where did that come from?
We have a population n of 160 million. Each individual has a probability of p=0.5 to vote for Ms Right and an equal probability q=0.5 to vote for Mr Wrong. This is a standard binomial distribution with standard deviation √(npq) = 0.5 * √(160 million) = 6,325. Plus or minus 99% corresponds to +/- 2.57 standard deviations = +/- 16,250 which we round up to 17,000.
The idea that the average voter is ignorant of politics is based on the idea that the personal cost of staying current with political issues is quite high, while the chances of any one person’s vote making a difference to outcomes is miniscule. It is therefore rational to not bother and stay ignorant (the theory is called Rational Ignorance).
Part 2
In Part 1 we encountered the theory of Rational Ignorance, the idea that your one vote makes such a small difference that it isn’t worth your time and effort to stay current with political issues. We showed that if the overwhelming mass of people voted at random, democracy would still work if a small elite were sufficiently educated and motivated to study the issues and vote the right way.
But we all know that it just isn’t so. People do not vote randomly: they are strongly opinionated and actively engaged with the issues – despite not studying them. In ‘The Myth of the Rational Voter’, Bryan Caplan flags three areas where voters passionately rally to counterproductive policies which actively harm their own self-interests.
1. Anti-Market Bias
Despite the fact that capitalism is the most successful form of economic organization ever seen on the planet, most people are profoundly suspicious of it. Adam Smith’s famous observation about the trade of the businessman: ‘By pursuing his own interest he frequently promotes that of society more effectually than when he really intends to promote it. I have never known much good done by those who affected to trade for the public good,’ has never been believed by the general public.
The public’s instincts are to go with regulation, price subsidies and Government provision of essential goods and services, believing that market mechanisms are driven by private greed, keep prices sky-high, lead to shoddy output and don’t give a damn about customers (i.e. themselves).
Given a competitive market, the truth of the matter is almost completely the reverse, as people would realize if they compared their grocery stores to almost any Government department they deal with. But people don’t trust markets.
2. Anti-Foreign Bias
Left to themselves, many people would choose to erect impenetrable tariff walls at our borders and keep all foreign imports out, stopping those perfidious foreigners stealing our jobs.
Protectionism misses a revelation about the gains from trade which has been known for 250 years. In a simple example suppose an American worker can make 10 cars or 5 bushels of wheat in a given time, while a Mexican can make 1 car or two bushels of wheat in the same time. Mexico is a poorer country, and not as productive as America. Obviously there is no point trading with them.
Take one American auto worker and one American farmer. Together they make 10 cars and 5 bushels of wheat.
Take three Mexican auto workers and one Mexican farmer. Together they make 3 cars and 2 bushels of wheat.
Total: 13 cars and 7 bushels of wheat.
Now let them specialize and trade. The 2 Americans make cars, 20 of them while the four Mexicans make wheat, 8 bushels of it.
Total: 20 cars and 8 bushels of wheat. So there is a point in trading with Mexico.
The Law of Comparative Advantage encourages countries to specialize in what they’re good at and trade with others doing the same. The result is prosperity, even if your trade partners damage their own economies through protectionism. Alternatively, you could be North Korea.
Does the general public buy this argument? Not at all. They listen to steel workers, about to lose their jobs because steel-making in America is uncompetitive, and they rally to their defense. Keep cheap steel imports out! In doing so, they make all other American goods which incorporate steel more expensive for themselves and less competitive on the world market. But, hey, we saved the steel workers!
Or did we?
3. Make-Work Bias
The third area where public opinion gets it wrong is layoffs. Capitalism works, and we all get richer, by continually churning obsolete technologies in favor of newer, more productive ones. In the short-term workers in these declining industries lose their jobs; in the longer term they tend to get new and higher-paid jobs. Still, we hear more about those unfortunates who don’t.
In 1800 it took 95 out of every 100 Americans to work the land to feed the country. In 1900 it took 40, while today it takes just 3 in a 100. That was a lot of farmers let go. Do you see them hanging around the poor parts of town begging for handouts? There was a lot of pain in the wrenching transitions which saw an agricultural economy transition to a modern technological one. At every step of the way, compassionate people cried ‘stop!’ – fighting to freeze the status quo and avoid redundancies.
Yet who today would want to go back?
---
There is a common factor to these three biases. Humans are social creatures: we have empathy with others in our social group. Our emotions reward efforts for the common good and prompt us to help those suffering misfortune and not stand idly by: that’s how we evolved.
Capitalism in its most effective, competitive mode deliberately pits people against people and disrupts bedded-down patterns of life in favor of disruptive change. Locally it can damage lives even as it globally increases prosperity and opportunity. Our emotions don’t ‘get’ the way complex, holistic capitalism works and in our guts we don’t really approve. And when it comes to elections (where the act of voting is very distant from any personal economic consequences) we vote our feelings.
Economist Bryan Caplan calls this ‘Rational Irrationality’ and it explains a lot about modern politics, even the forced-hypocrisy of otherwise honest politicians who are forced to advance correct policies by stealth in the face of heated populist opposition.
---
Further Reading: The Myth of the Rational Voter, Bryan Caplan (2007), Princeton University Press.
---
Opinion Polls
As a bonus here’s a quick review of how opinion polls work. The pollsters ask, say, 1,000 people if they intend to vote for Ms R. Let’s suppose she’s languishing a little and the poll gives her 33% support. The standard deviation of this sample of voters is, as before, √(npq). We could use p=0.3, q=0.7 but it’s easier and more conservative just to stick with p=q=0.5 so that √(pq) = 0.5, its maximum value.
So one standard deviation σ is 0.5 * √(1,000) = 0.5 * 31.6 = 15.8.
The 95% confidence interval around 33% is +/- 1.96 standard deviations, 1.96 * 15.8 = 31. The pollsters express this as a percentage, 31/1,000 = 3.1% and they tell you that Ms R is currently polling at 33% with a margin of error of plus or minus 3.1%.
They’ll be right nineteen times out of twenty.
---
[Note: this was originally going to be a couple of articles for sciencefiction.com but I think you'll agree that they're better here.]
Did you read the SF story where a future population is oppressed by ... well, an evil oppressor, and the time-travelling hero hands the Resistance the ultimate secret weapon, the U.S. Constitution? It may be one of the classic SF clichés but it speaks to a bedrock conviction of American culture: Democracy is Wonderful.
In truth it would be wonderful if the average voter wasn’t so incredibly ignorant:
- Half of Americans don’t know that each state has two senators
- Less than 40% know their representatives’ party affiliations
- 45 percent of adult apparently believe that Karl Marx’s communist principle “from each according to his abilities, to each according to his needs” is actually part of the U.S. Constitution.
How on earth do we get sensible representatives, policies and decisions from this ocean of baleful ignorance? We know that most voters don’t understand micro- or macroeconomics and fail to appreciate the subtleties of foreign policy. Given the likely effects of their one vote, it’s even been argued that it’s rational for them not to invest much time in preparing for an act of so little practical consequence.
Thankfully, it might not be quite as bad as it appears.
An example: suppose the 160 million registered voters in North America have to vote for candidates Ms Right or Mr Wrong. If we have 160 million totally ignorant citizens, then Ms R and Mr W are each going to get around 80 million each and it’s a toss-up as to who will win. We can model this outcome by simply imagining that each voter has a coin and just flips it: Heads for Ms R and Tails for Mr W.
If we imagine running that vote over and over again, then 99% of the time the total number of votes cast for each candidate would each be within 17,000 of the magic balancing 80 million votes. That is, when we counted the number of votes for Ms Right (or Mr Wrong) then with 99% probability the number of votes she or he would receive would lie between 80 million minus 17,000 and 80 million + 17,000. This is the magic of statistics* and also a possible answer to our dilemma.
Suppose that there are 20,000 well-educated and politically-engaged people in the electorate (just 0.0125% of the 160 million voting population).
Suppose that Ms Right’s political supporters successfully persuade these 20,000 to vote the Right way. A result! Democracy has successfully delivered the goods, despite an electorate which scarcely deserves such good fortune. So Democracy works even with overwhelming public apathy and ignorance if there is tiny crowd of political sophisticates who take the trouble to research the issues and vote the right way.
But be cautious: is this a model which describes the actual political world we see around us? Sadly not: in point of fact the true situation is even worse than widespread and systemic ignorance. The population-at-large in fact exhibits perverse and self-defeating bias: the people systematically vote for candidates and policies which act against their own self-interests.
How and why this happens will be discussed in part 2 of this article below.
---
* Where did that come from?
We have a population n of 160 million. Each individual has a probability of p=0.5 to vote for Ms Right and an equal probability q=0.5 to vote for Mr Wrong. This is a standard binomial distribution with standard deviation √(npq) = 0.5 * √(160 million) = 6,325. Plus or minus 99% corresponds to +/- 2.57 standard deviations = +/- 16,250 which we round up to 17,000.
The idea that the average voter is ignorant of politics is based on the idea that the personal cost of staying current with political issues is quite high, while the chances of any one person’s vote making a difference to outcomes is miniscule. It is therefore rational to not bother and stay ignorant (the theory is called Rational Ignorance).
Part 2
In Part 1 we encountered the theory of Rational Ignorance, the idea that your one vote makes such a small difference that it isn’t worth your time and effort to stay current with political issues. We showed that if the overwhelming mass of people voted at random, democracy would still work if a small elite were sufficiently educated and motivated to study the issues and vote the right way.
But we all know that it just isn’t so. People do not vote randomly: they are strongly opinionated and actively engaged with the issues – despite not studying them. In ‘The Myth of the Rational Voter’, Bryan Caplan flags three areas where voters passionately rally to counterproductive policies which actively harm their own self-interests.
1. Anti-Market Bias
Despite the fact that capitalism is the most successful form of economic organization ever seen on the planet, most people are profoundly suspicious of it. Adam Smith’s famous observation about the trade of the businessman: ‘By pursuing his own interest he frequently promotes that of society more effectually than when he really intends to promote it. I have never known much good done by those who affected to trade for the public good,’ has never been believed by the general public.
The public’s instincts are to go with regulation, price subsidies and Government provision of essential goods and services, believing that market mechanisms are driven by private greed, keep prices sky-high, lead to shoddy output and don’t give a damn about customers (i.e. themselves).
Given a competitive market, the truth of the matter is almost completely the reverse, as people would realize if they compared their grocery stores to almost any Government department they deal with. But people don’t trust markets.
2. Anti-Foreign Bias
Left to themselves, many people would choose to erect impenetrable tariff walls at our borders and keep all foreign imports out, stopping those perfidious foreigners stealing our jobs.
Protectionism misses a revelation about the gains from trade which has been known for 250 years. In a simple example suppose an American worker can make 10 cars or 5 bushels of wheat in a given time, while a Mexican can make 1 car or two bushels of wheat in the same time. Mexico is a poorer country, and not as productive as America. Obviously there is no point trading with them.
Take one American auto worker and one American farmer. Together they make 10 cars and 5 bushels of wheat.
Take three Mexican auto workers and one Mexican farmer. Together they make 3 cars and 2 bushels of wheat.
Total: 13 cars and 7 bushels of wheat.
Now let them specialize and trade. The 2 Americans make cars, 20 of them while the four Mexicans make wheat, 8 bushels of it.
Total: 20 cars and 8 bushels of wheat. So there is a point in trading with Mexico.
The Law of Comparative Advantage encourages countries to specialize in what they’re good at and trade with others doing the same. The result is prosperity, even if your trade partners damage their own economies through protectionism. Alternatively, you could be North Korea.
Does the general public buy this argument? Not at all. They listen to steel workers, about to lose their jobs because steel-making in America is uncompetitive, and they rally to their defense. Keep cheap steel imports out! In doing so, they make all other American goods which incorporate steel more expensive for themselves and less competitive on the world market. But, hey, we saved the steel workers!
Or did we?
3. Make-Work Bias
The third area where public opinion gets it wrong is layoffs. Capitalism works, and we all get richer, by continually churning obsolete technologies in favor of newer, more productive ones. In the short-term workers in these declining industries lose their jobs; in the longer term they tend to get new and higher-paid jobs. Still, we hear more about those unfortunates who don’t.
In 1800 it took 95 out of every 100 Americans to work the land to feed the country. In 1900 it took 40, while today it takes just 3 in a 100. That was a lot of farmers let go. Do you see them hanging around the poor parts of town begging for handouts? There was a lot of pain in the wrenching transitions which saw an agricultural economy transition to a modern technological one. At every step of the way, compassionate people cried ‘stop!’ – fighting to freeze the status quo and avoid redundancies.
Yet who today would want to go back?
---
There is a common factor to these three biases. Humans are social creatures: we have empathy with others in our social group. Our emotions reward efforts for the common good and prompt us to help those suffering misfortune and not stand idly by: that’s how we evolved.
Capitalism in its most effective, competitive mode deliberately pits people against people and disrupts bedded-down patterns of life in favor of disruptive change. Locally it can damage lives even as it globally increases prosperity and opportunity. Our emotions don’t ‘get’ the way complex, holistic capitalism works and in our guts we don’t really approve. And when it comes to elections (where the act of voting is very distant from any personal economic consequences) we vote our feelings.
Economist Bryan Caplan calls this ‘Rational Irrationality’ and it explains a lot about modern politics, even the forced-hypocrisy of otherwise honest politicians who are forced to advance correct policies by stealth in the face of heated populist opposition.
---
Further Reading: The Myth of the Rational Voter, Bryan Caplan (2007), Princeton University Press.
---
Opinion Polls
As a bonus here’s a quick review of how opinion polls work. The pollsters ask, say, 1,000 people if they intend to vote for Ms R. Let’s suppose she’s languishing a little and the poll gives her 33% support. The standard deviation of this sample of voters is, as before, √(npq). We could use p=0.3, q=0.7 but it’s easier and more conservative just to stick with p=q=0.5 so that √(pq) = 0.5, its maximum value.
So one standard deviation σ is 0.5 * √(1,000) = 0.5 * 31.6 = 15.8.
The 95% confidence interval around 33% is +/- 1.96 standard deviations, 1.96 * 15.8 = 31. The pollsters express this as a percentage, 31/1,000 = 3.1% and they tell you that Ms R is currently polling at 33% with a margin of error of plus or minus 3.1%.
They’ll be right nineteen times out of twenty.
---
[Note: this was originally going to be a couple of articles for sciencefiction.com but I think you'll agree that they're better here.]
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