Conservapedia:Schlafly Statistics

There are three kinds of lies: lies, damned lies, and statistics.

Schlafly Statistics is one of the favorite "debating tactics" of Andrew Schlafly, the founder and braindead braintrust behind Conservapedia. It consists of making up an utterly spurious and unfounded correlative statistics in a bizarre attempt to undermine the argument of someone who is completely schooling him, usually but not always an expert in the field under discussion. This is basically a clumsily disguised ad hominem attack, and even if the "statistic" were true, it would have no bearing on the matter at hand.

Schlafly statistics are infallible as Andrew Schlafly has taken twice as many statistics courses as anyone else — he is extremely modest, too.

The National Academy of Science has described the attempt to publish Schlafly Statistics as such:

The issues raised by Mr. Schlafly are neither obscure nor subtle, but are part of everyday statistical analysis at a level too elementary to need rehearsal in the pages of PNAS.

Examples
Here are but a few examples of this fascinating form of brain damage.

Wikipedia Statistics
Aschlafly had the genius idea to post this:

Andy here fails at basic math. To quote, "Polls show that about twice as many Americans identify themselves as "conservative" compared with "liberal", and that ratio has been increasing for two decades. But on Wikipedia, about three times as many editors identify themselves as "liberal" compared with "conservative". That suggests Wikipedia is six times more liberal than the American public." We shall now proceed to utilize the power of mathematics to demonstrate that this is wrong.

Andy tries to justify this by using something he calls "liberal quotient". This is the ratio of liberals in the group to conservatives. In America, this would be 1:2, and on Wikipedia, 3:1. Apparently, Andy has somehow neglected to realize that a ratio expressed in this form is not a fraction, and that the fractional description of the portion of a group composed of n, out of a group composed of n and q, is not actually n/q, it's n/(n+q). Therefore, instead of dividing 3/4ths (3:1 = 75%) by 1/3rd (1:2 = 33.333...%) which gives 2.25, he divides 3/1 by 1/2 and gets 6. In other words, he's said that 300% of Wikipedia editors are liberal. As such, this statistic is entirely fallacious.

Chim-chim-chimerae
Show me a chimney sweep and I will show you someone with 100% (!) dirty nails.

Franklin Stoven In
From a discussion of Benjamin Franklin's religious views on his talk page:

At least he is correct on that last point — with regards to his own closed mind, that is.

Now he's up to 95% certainty. He just keeps getting better! Is he aware that scientists consider a 90% or 95% correlation very significant when it is a correlation of data collected from actual observations or experiments rather than just plucked out of the air by one person based on intuition alone? Aschlafly didn't go into the details of all the people he has observed these data on; which ones advocate classroom prayer, which ones accept evolution, and which ones believe Franklin was a deist.

A three-fer!
In a main page talk discussion of Ron Paul and evolution, Mr. Schlafly loads for bear and uncorks this unsubstantiated beauty:

When the ratio is 1 in 100, that 1 still exists
We can infer the religion of a person by the majority religion of their countrymen 20 years after their death. The relevant factors to consider when inferring someone's religion do not include: the assertions of their (liberal) family members, or the fact that they were Harvard-educated, but only their nationality. The possibility that a person would hold a minority religious viewpoint is inconceivable.

Schlafly uses this method in order to create the impression that when someone makes a claim that is only true 1% of the time, it means there is a 99% chance they are lying. To turn this around on him, he claims to have a degree in electrical engineering as well as in law. Certainly far less than 1% of the population of the United States has both these degrees, therefore the probability that he is lying about this is much greater than 99%. The probability of him being gay or Muslim is much, much higher. (Additionally, he claims to be the son of Phyllis Schlafly, the odds of which are about 75,000,000 to 1, or in the neighborhood of .000001% likely.)

Just to explicitly say why this is wrong, if you haven't cottoned on yet, the point is that if someone says they are description X, it is much more likely that they are X than it would be if you just chose someone at random. For instance, very few people have a sister named Carmelita, but if someone says they have a sister named Carmelita, it's still likely that they're telling the truth, because the number of people who lie about having a sister called Carmelita is incredibly small.

Probability the Schlafly way
On the list of Counterexamples to Evolution, Schlafly showed how, using a list of statements that may or may not be true, overall the list must be true.

The first major problem is the assumption that the 14 items are independent, which they of course are not, as some or all are true if evolution is incorrect and all are false if evolution is correct. The second problem is that 1-(0.95)^14 is approximately 51%, which most people wouldn't describe as "nearly 100%". His third problem is he just made the 5% figure up, and it could be less. Finally, the problem we have seen many times in Schlafly's arguments, is that the truth is not a statistical function that can be determined by getting close enough to 100%. So long as the probability is not 0%, there still exists a chance that it might be true.

Sampling
Sampling is a process by which a representative subset of a population is studied in order to infer characteristics of the population as a whole. This is typically employed when the population would be too large to study, or when it's determined that the subset would provide acceptable levels of accuracy. The sample size must be representative if accurate inferences are to be made. For example, if wanting to understand the prevalence of beards in the population of London, examining just 10 chins is unlikely to yield representative results. The makeup of the sample is just as important. Would the results of a survey be useful if female chins were included, or if the men questioned all happened to be emerging from a local mosque? Schlafly does not trouble himself with such concerns.

In March 2011 Schlafly trumpeted the news that Wikipedia was only retaining 12% of its editors. The fun began when Schlafly was asked for details on Conservapedia's editor retention rate.

Although clearly an informal and quick response to the question, the mere fact that he chose to give this answer demonstrates dishonesty or a complete misunderstanding of statistical analysis. He has arbitrarily chosen three "quality editors" who are what he describes as being "frequent editors". By this logic any wiki could claim a 100% retention rate.

Mystery: Why Do Non-Conservatives Exist?
Andy did a "statistical analysis" on the "mystery" on why non-conservatives exist. He expanded his "analysis" over the period of a year with such insights as "15%: refuse to forgive themselves and let go of their past mistakes and image" and "10%: refuse to rise above their personal temptations, often self-destructive, and hate conservatives who criticize their self-indulgent behavior".

One author who observed that these numbers were, maybe, not entirely backed up by data, and that making up "facts" does not exactly further the conservative cause, was promptly reverted.

He later added a helpful "statistical analysis" on "triggers reconsideration of liberal beliefs".

Andy — being the intellectually honest man that he is — is aware that his methods may not be perfect, as he states that his numbers are merely estimates.

Statistical contagion
And it is infectious — here TK experiments with the new toy, in another context:

In a similar vein,

PS: that's not a footnote asterisk, it's an "emphasis" asterisk, in case you aren't familiar with this particular Sysop's style.

Hypothesis testing
Whilst more sophisticated statistical methods require testing to see if results and conclusions are valid, Schlafly statistics require only Andy to look at the numbers and he can see the difference:

Data mining
Schlafly statistics also differs from frequentist or Bayesian statistical methods in that data mining is not needed:

Applications
Schlafly statistics are superior to any other method of analysing data as it would save millions of lives:

Regression
Another advantage of Schlafly statistics is the ability to perform any kind of regression fit to the data and achieve a perfect fit, regardless of the data's pattern.

Example: fitting a geometric line through data.
 * 1) Announce that your data has a geometric growth, despite the fact that it is linear.
 * 2) Constantly add hundreds of selected data points, keeping a meticulous count, until you achieve a geometric-like growth.
 * 3) Remove any data that upsets this geometric growth.
 * 4) Announce that this geometric growth applies to nearly everything.

Study analysis
So, x=Time spent studying; Period between 1500 and 1700 = y; Period between 1700 and 1877 = z. Rebuilding Schlafly's equation, we get the ideal value of x=z*0.9-y*0.9. No, wait. If y*0.9=x, then …no, hang on. I'll start again. X is 90% of 1877. No, that's not right. Wait. The difference in years of y=200, and the time period of z is 177. So y>z. So the factor of 90% can't be fairly applied to both periods. Can it? I don't know, I give up.

What he fails to understand here is that X = 7 and v = 9, therefore, 90% of questions will in fact be from the year 1880, making your studying of anything, especially from a liberal university, useless.

Corollary
A corollary to the Schlafly Statistic is the Schlafly Study, where Andy claims that his views are supported by "a majority of studies", and since this is so obvious, there is no need to cite them.

What Mr. Schlafly apparently does not understand (and what was pointed out to him in the same discussion) was that all significant studies show an increased risk because that is the definition of significance. A non-significant result only allows the conclusion "no significant effect was found" However if multiple studies are unable to find a significant effect despite superior levels of statistical power this suggests significant findings are due to random variation in the sample (i.e. the finding of significance is the product of random variation between the two conditions).