Our Canuk, one Bill Whatcott, distributed a flyer which said that a certain man who pretends to be a woman … Continue Reading Canadian Man Fined $55K For Calling Biological Male Biological Male
Here it is! The one, the only, the peer-reviewed (and therefore true) “Reality-Based Probability & Statistics: Solving the Evidential Crisis” … Continue Reading Reality-Based Probability & Statistics: Ending The Tyranny Of Parameters!
It’s White Privilege Week! What are you doing to celebrate? Twitter started off a day early by forcing the tag … Continue Reading It’s White Male Privilege Week!
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Don’t miss the Nature article below! (I stole the graphic from it.) Am Stat Here are the opening lines from … Continue Reading Death Blow To Statistical Significance! — Bonus: Here’s The Replacement
Yours Truly is a statistician; indeed, the Statistician to the Stars! I call myself that because I perform probability analyses … Continue Reading There Are No Such Thing As Gays (Or Trannies)
Our Canuk, one Bill Whatcott, distributed a flyer which said that a certain man who pretends to be a woman is a “biological male.” The man who would be a woman, but is in fact a biological male, took exception to this and had our Canuk hauled in front of one of Canada’s “rights” kangaroo courts.
Whatcott had brought a doctor with him to certify that the man who pretended to a woman was, indeed, a biological male. Here, according to Lifesite, is what the judge, who if this were America we’d suspect was an Affirmative Action appointee, said:
Tribunal judge Devyn Cousineau, however, ruled “the ‘truth’ of the statements in the flyer is not a defense.”
“Therefore, to the extent that Mr. Whatcott intends to call witnesses to establish the truth of his impugned publications, that evidence is simply not relevant to the legal issue and will not be heard by this Tribunal,” she wrote.
Nice scare quotes around truth, eh?
Now it’s not unnatural to suppose Cousineau took one too many pucks to the head. But it’s a better bet her raving ideological stance is sincere. For she also wrote “even questioning transgenderism is discriminatory.” There’s the state of Canadian science, right there. “Discrimination” was the real reason for the fine (or most of it).
“‘[T]he proposition that we should continue to debate and deny the existence of trans people is at the root of the prejudice and stereotypes that continue to oppress them,’ wrote Cousineau.”
“‘Throughout his testimony, Mr. Whatcott refused to recognize Ms. Oger as a woman, or to abide by the Tribunal’s frequent orders not to call her a man,’ she wrote in a footnote.” Stones.
There no such things as “trans people”, of course. Not in any essential sense. There are lots, and growing numbers, of men pretending to be women, and women pretending to be men. And some even elevate the pretending to full scale delusion. These people exist, all right.
Prejudice should be applied to those who are not quite all there. Prejudice in this sense is a good thing. I mean, you wouldn’t want these people to, say, read books to your kids at story hours at libraries, and in doing so encourage your own flesh and blood to abandon reality for fantasyland. You’d love them far too much for that. Right?
Stereotypes are also important—and largely true. Can you, even if you were in support of sexual LARPing, think of a stereotype about a man pretending to be a woman that is not true? Enter it below.
Anyway, what’s with all this negativism about stereotypes? They are largely true everywhere they are applied. They are useful and almost always correct distillations of information about certain groups of people. Even people like bloggers.
Never mind that. Whatcott is going to be out 55-thousand loonies. We are not the first to recognize the appropriateness of this nickname. It’s a lot of money. Imagine it’s you. Imagine you called a biological male who is a biological male a “biological male”, and this biological male then caused you to pay an extraordinary sum for saying a simple truth. It would sting, wouldn’t it?
And it would cause other people inclined to tell the truth about Reality to keep their mouths shut.
Now my ignorance of Canadian jurisprudence is vast, so I have no idea how this will all play out in its courts. Maybe Whatcott can convince some higher-level entity that Reality is still a going concern. Maybe not.
Either way, it’s clear that Canada, at least at this level, is willing to use violent means to force people to deny Reality. This is not violence on the level of thumbscrews, but it is violence nonetheless, because Cousineau’s punishment has the full force of the entire Canadian government behind her. Whereas Whatcott is one man, or one man plus one lawyer.
There is no disincentive for Cousineau not to threaten or use violent means. What would be such a disincentive is clear enough. But we’re not quite there yet.
Here it is! The one, the only, the peer-reviewed (and therefore true) “Reality-Based Probability & Statistics: Solving the Evidential Crisis” (the link is to the pdf, which is 11 MB; there are many pictures).
This a large review paper, summing up the problems I see in statistics, with a guide of how escape from the void.
There is no question computer scientists are kicking statisticians’ asses. Hard. As we saw yesterday. The answer is anyway simple: “AI”, which is nothing but lists of if-then statements, at least divorces, or does not concern itself much with, parameters. Statistics believes these strange entities have life! All practice, frequentist or Bayes, revolves around them. We are in orbit around a fiction.
Enough already! Let’s turn our eyes toward Reality. Here’s how.
Section 2: NEVER USE HYPOTHESIS TESTS
These are refinements to “Everything Wrong With P-values Under One Roof“, here with a mind toward cause.
P-values are now officially dead. The sooner we stop talking about them and about Reality, the better.
Section 3: MODEL SELECTION USING PREDICTIVE STATISTICS
Do we need hypothesis tests? No. And we only need model selection sometimes. If we’re forced to pick between models—and since most models are free in the sense they are only bits of code, we don’t always have to pick—then we should pick with a Reality-based metric and nothing else. Sometimes models cost model, because observations cost money, and therefore we will need to select. We do this based on Reality, not parameters.
Regular readers will be familiar with the mechanics of predictive inference, probability leakage, and all that. So you can skim this section, but pay some attention to the example.
Section 4: Y CAUSE
This is it! This is the missing element. The lack of focus on cause.
Parameter estimates are often called “effect size”, though the causes thought to generate these effects are not well specified. Models are often written in causal-like form (to be described below), or cause is conceived by drawing figurative lines or “paths” between certain parameters.
Parameters are not causes, and causes don’t happen to parameters. Probability is not real. Thus cause cannot operate on it. Parameters aren’t real: same deal.
Cause is probability and statistics is mixed up, to say the least; right ideas mix with wrong and swap places. There is no consistency.
Cause is conditional. Three small words packed with meaning. All probability is conditional, too, and in the same way. Once this is understood, we have made a great leap, and we can see what is possible to know about cause and what is not.
Section 5: TRUST BUT VERIFY
“Scarcely any who use statistical models ever ask does the model work? Not works in the sense that data can be fit to it, but works in the sense that it can make useful predictions of reality of observations never before seen or used in any way. Does the model verify?”
Then some ways this can and must be done.
Section 6: THE FUTURE
“No more hypothesis testing. Models must be reported in their predictive form, where anybody (in theory) can check the results, even if they don’t have access to the original data. All models which have any claim to sincerity must be tested against reality, first in-sample, then out-of-sample. Reality must take precedence over theory.”
WHERE?
This is in the inaugural edition of the Asian Journal of Economics and Banking, which does not yet have a web site (it’s that new). Paper copies are available at all better libraries.
Here’s the headline: “AI can predict when someone will die with unsettling accuracy: This isn’t the first time experts have harnessed AI’s predictive power for healthcare.”
Unsettling accuracy? Is accuracy unsettling? Has AI progressed so far that all you have to do is step on an AI scale and the AI computer spits out an unsettlingly accurate AI prediction of the AI end of your AI life? AI AI AI AI AI? AI!
I’ve said it many times, but the marketing firm hired by computer scientists has more than earned its money. Science fiction in its heyday had nothing on these guys. Neural nets! Why, those are universal approximators! Genetic algorithms! Genes in the machine. Machine learning! Deep learning! Like, that’s deep, man. Artificial intelligence! Better than the real thing!
What has statistics got? Statistically significant? No, that’s dead. Thank God. Uniformly most powerful test? Unbiased estimator? Auto-regressive? Dull isn’t in it. You won’t buy any headlines talking about mu-hat.
What’s the difference between statistics and AI? Besides the overblown hype, that is? One thing: a focus on the results. That’s the reason AI is landing every punch, and why statistics is reeling. Statistical models focus on fictional non-existent hidden unobservable parameters, whereas AI tries to get the model to make good predictions of reality.
Now AI is nothing but statistical modeling appended with a bunch of if-then statements. Only this, and nothing more. Computers do not know as we know; they do not grasp universals or understand cause. They don’t even know what inputs to ask for to predict the outputs of interest. We have to tell them. Just as we do in statistics.
The reason AI models beat statistical ones is because AI models are tuned to making good predictions, whereas statistical models are usually tuned to things like wee p-values or parameter estimates. Ladies and gentlemen, parameters are of no interest to man or beast. The focus on them has forced, in a way, a linearity culture, whereas if we can’t write down the model in pleasing parameterized form, we’re not interested. Besides, we need that form to do the limit math of statistics of estimators of these parameters so that we can get p-values, which do not mean what anybody thinks they do.
AI scoffs at parameters and says, how can I create a mathematical function, however complex, of these input measures so that skillful, but not over-fit, predictions of the output measure are good?
That, and its understanding, or its attempts at understanding, cause. We’ve discussed many times, and it’s still true, that you can’t get cause from a probability model. Cause is in the mind, not the data. We need to be part of the modeling process. And so on. AI, though it’s at the beginning of all this, tries to get this right. I’ll have a paper tomorrow on this. Stay tuned!
I say AI will never make it. Computers, being machines, aren’t intellects; they are not rational creatures like we are. Intellect is needed to extract universals from individual cases, and computers can never do that—unless we have first programmed them with the answer, of course.
That is to say, strong AI is not possible. Others disagree. To them I say, don’t wait up.
We can’t discount the over-blownness of the comparison. Reporters love AI, and nearly all cherish the brain-as-computer metaphor, so we’ll apt to see intellect where it is not. Plus hype sells. Who knew?
It’s not all hype, of course. AI is better, in general, at making predictions. But headlines like the one above are ridiculous.
When all the number crunching was done, the deep-learning algorithm delivered the most accurate predictions, correctly identifying 76 percent of subjects who died during the study period. By comparison, the random forest model correctly predicted about 64 percent of premature deaths, while the Cox model identified only about 44 percent.
These are not unsettling rates. The “deep learning” is AI, the “random forest” is “machine learning” (if you like, a technique invented by a statistician), and “Cox model” is regression, more or less. I didn’t look at the details of how the regression picked its variables, but if it’s anything like “stepwise”, the method was doomed.
We always have to be suspicious about the nature of the predictions, too. These should be on observations never before see or used in any way. They should not be part of a “validation set”, because everybody cheats and tweaks their models to do well on the validation set, which, as should be clear, turns the validation set into an extension of the training set.
It’s White Privilege Week! What are you doing to celebrate?
Twitter started off a day early by forcing the tag #MyWhitePrivilege to trend.
As the multitude of links in this thread from Everything Oppresses prove (and this one; regular readers will recognize most of them), there is no force as potent in the universe as White Men. All want to be like us and have what we have. Yet all fear us and hate us. Why? Because we control everything. We have the power, in every institution, in every hallway, in every situation. Things happen because we will it, and for no other reason.
How do we wield this majestic force? Not directly: not by contact. By our presence.
God Himself, the most powerful and privileged among All and Creator of All, must therefore be a white man. Just look at the pictures!
As part of the White Male Privilege Week festivities, those who are non-white should refrain from commenting. This includes honorary non-white men.
The reason for this proscription is obvious. White men have the power to deny the existence of non-white-males. By restraining comments from non-whites, we make this power actual.
This may seem like a bad thing. It’s the opposite! This is the best gift we can give to the benighted people unfortunate enough not to have been born white and male.
As this sage article well argues, a new culture has developed in the West. That of The Victim. Victimhood denotes top status, appeal, self-worth, meaning “in much the same way that being recognized for bravery did in honor cultures.” There is no person as privileged as The Victim—after granting white men unimpeachable all-time First Place, of course.
Everybody wants to be a Victim; everybody longs for Victimhood. Except white men, of course. We are Victim makers.
Consider: If there is nothing better than being a Victim, and if white men have the power to do all bad things, it is the job of the white man to provide this victimhood to oppressed non-white-men everywhere. It is our sacred duty.
And, fellow white men, I must tell you, in brutal honesty, that most of us have been failing in this duty.
Yes. There are not nearly enough true Victims. Where is our sense of noblesse oblige!?
What we have now is a mere shadow of Victimhood. We have sad fat pimply purple-haired ladies, scrawny soyboy sodomites of color, skank spoiled rich (but non-white) writers for major newspapers, latinx flared-nostril wild-eyed boyfriendless cat ladies, and many more running around claiming to have suffered microaggressions from white males.
White Men: microaggressions? Is that the best we can do? It takes one million microaggressions to equal one true aggression. Yet it only takes one true aggression to make a Victim-For-Life. We are White Men! Let us call upon the power that has been granted us and have pity on the lesser sex and races! Our subjects cry out for real aggressions. Not these namby-pamby fictional fantasies non-whites are forced to cook up in their understandable quest for noble Victimhood.
We’re letting our side down, men. Social justice is moving off campus and to every facet of life. Look for it at your employer soon, if it’s not already there. The need for new Victims will only increase. If we do not get on this right away, there will not be enough Victims to go around—especially considering the number of non-white men being allowed into the country. Why, it will soon only be a handful of us white men left. And we’ll still be expected to make Victims of everybody else.
The solution to this crisis is to make true Victims of the victim wannabes.
Since true Victimhood is the goal and desire of every right thinking person, the more folks we can put into the Victim camp using our awesome white male powers, the more they will recognize our true gifts. Thus the more they will thank us. The more they will love us.
Come, white men. Celebrate White Male Privilege Week in the way it was meant to be.