William M. Briggs

Statistician to the Stars!

Gravitational Waves And Discovering Cause


You’ll have heard by now that the existence of gravitational waves have been “confirmed.” The scare quotes are intentional, but not in the sense that I (necessarily) disbelieve in the confirmation; instead, they’re used to highlight that confirmation-as-decision is a dicey subject.

The gist on the discovery is this: deep in space, a pair of black holes decided to mate, a process which sent ripples of spacetime cascading our way; these waves hit the Laser Interferometer Gravitational-Wave Observatory, or LIGO, and the machine that goes Bing! sprang into action.

The LIGO itself is in two parts, one in Washington (not DC, which is probably why it worked) and the other in Louisiana. Precisely:

Each detector is shaped like a giant L, with legs four kilometers long. Laser light bounces back and forth through the legs, reflecting off mirrors, and amazingly precise atomic clocks measure how long it takes to make the journey. Normally, the two legs are exactly the same length, and so the light takes exactly the same amount of time to traverse each. If a gravitational wave passes through, however, the detector and the ground beneath it will expand and contract infinitesimally in one direction, and the two perpendicular legs will no longer be the same size. One of the lasers will arrive a fraction of a second later than the other.

LIGO must be unbelievably sensitive to measure this change in the length of the legs, which is smaller than one ten-thousandth the diameter of a proton, or less than the size of a soccer ball compared with the span of the Milky Way…”There are so many knobs to turn, so many things to align, to achieve that [sensitivity].” In fact, the experiment is so delicate that unrelated events such as an airplane flying overhead, wind buffeting the building or tiny seismic shifts in the ground beneath the detector can disturb the lasers in ways that mimic gravitational signals. “If I clap in the control room, you will see a blip,” says Imre Bartos, another member of the LIGO team at Columbia.

This is even more sensitive than a teenage female is to Facebook postings. The extreme touchiness is why “researchers carefully weed out such contaminating signals and also take advantage of the fact that the detectors in Washington and Louisiana are highly unlikely to be affected by the same contamination at the same time.”

Highly unlikely is not, of course, equivalent to impossible. It—highly unlikely—isn’t even definable on its own, and is in this case stated in circular terms. Why? Because all probability is defined only with respect to certain evidence. Here that evidence is tacit, and includes the idea that either gravitational waves are real or that nothing else exists that can cause matched measurements at the two detectors. I am in no way claiming that such other causes exists, but I have no proof that one doesn’t.

Nancy Cartwright recently made the same point about current tests in space, using Gravity Probe B, for the predicted geodetic and frame-dragging effects caused by the mass of the earth as it flies through space.

Consider the Stanford Gravity Probe Experiment, which put four gyroscopes into space to test the prediction of the general theory of relativity that gyroscopes should precess due to coupling with space-time curvature. The Gravity Probe prediction about its gyroscopes was about as free of condition as any claim in physics about the real world could be. That’s because the experimenters spent a vast amount of time — over twenty years — and exploited a vast amount of knowledge from across physics and engineering. They tried to fix it so that all other causes of precession were missing; hence all the other causes would be, ipso facto, describable in the language of physics. Moreover if they had not succeeded and other causes occurred, then any that they couldn’t describe would make precise prediction impossible. If you can’t describe it, you can’t put it into your equations.

Same situation. There may be some other cause, or causes, that knock the gyroscopes akimbo that are unrelated to space-time curvature. Again, I make no claims that any such cause exists. I certainly haven’t evidence one does. But the point remains: everything witnessed at LIGO or Gravity Probe B is seen through the filter of theory, which is to say, on certain premises accepted as true.

There have been in science “discoveries”—and here the scare quotes fulfill their usual sardonic purpose—that were later proven to have been signals caused by something other than the proposed cause. Cold fusion immediately comes to mind. Before everybody understood what was happening, cold fusion was not well supported by non-observational evidence, which is to say, by theory. Yet some scientists believed what theory was there was true, others didn’t. The ones that didn’t held to other theories (about what was causing excess heat in some test tubes). Either way, theory was there.

Again either way, those theories might not have been as universal as proponents thought. Cartwright emphasizes that many theories, perhaps most, are only held ceteris paribus, meaning they are not universal and without exception. And that is just another way of saying the theories are incomplete.

Short way to think of this: observations and the equations (or theory) said to produce the observations can’t prove cause. Proving cause comes when we understand the true nature or essence of things involved. True or full understanding of thing’s essence does not come easy.

Update This article by Feser is apropos: ‘“Brute Fact View” according to which the universe simply exists without explanation, and that’s that.

Regression Can’t Prove Discrimination


Anybody out there a lawyer, or do you know one, who is involved on the side of angels in these so-called discrimination lawsuits? Have them contact me.

The argument that complainers (usually our government) are using to “prove” discrimination are statistical nonsense. This is easy to prove. Problem is, those who are defending against these charges also appear to believe statistics can prove discrimination, and instead of arguing that it doesn’t, they instead attack the data or say different statistics can prove lack of discrimination. All this is wrong.

Two recent examples: “Obama bullied bank to pay racial settlement without proof: report“.

So CFPB applied the screws to Ally, saying it had “statistical evidence” showing its participating dealers were “marking up” loan prices for blacks and Hispanics vs. whites (by an average of $3 a month). Ally fought back, insisting non-discriminatory factors, such as credit history, down payments, trade-ins, promotions and rate-shopping, explained differences in loan pricing. After conducting a preliminary regression analysis, the bank found these factors alone accounted for at least 70 percent of the “racial disparities” the government was claiming.

Second: “Obama’s new equal pay executive action distorts the definition of equality“.

On Friday, the Obama administration announced executive action that would require companies with 100 employees or more to report to the federal government how much they pay their employees broken down by race, gender, and ethnicity. The proposed regulation is being jointly published by the U.S. Equal Employment Opportunity Commission and the Department of Labor. It is hoped that this transparency will help to root out discrimination and reduce the gender pay gap..

You have a sample of incomes, or loans, or whatever, given or measured on people of two races, J and K. You run a regression with incomes (or whatever) on race, which gives a wee p-value on race. What has been proved?

Nothing. Or, rather, it proves that given this data and the ad hoc, crude assumption of a normal to quantify income, and assuming the spread parameter of this normal for both races is equal, if the central parameter for race (or race difference) was certainly equal to 0, then the probability of seeing a p-value in the test used on the parameter larger than the one seen if the experiment which gave rise to the data could be embedded in an infinite sequence of such experiments is wee.

And that is all that is proved. (That mouth-numbing sentence is the definition of a p-value.)

Does the wee p-value prove that racism exists? No. Does it prove that race J makes more than race K? No. What does the regression say about what caused the observed “discrepancy”? Nothing. Not. One. Thing. As in nothing. Zero. Nada. Nothing.

How do I know this? Because of the Banana Test. Every person in the sample, because they are human, have a mind-boggling number of things we could have measured on them, besides their race. Something like this will therefore almost surely be true: the folks in race J will have eaten over their entire lives at least one banana more than did the folks in K. Therefore in the regression we need not have labeled the groups by the two races, J and K, but instead by the equally true High Bananas and Low Bananas.

It may not be bananas, but number of Lego blocks owned, or has breathed in more air downstream from Cleveland, or number of bubbles blown, or on and on and on and on some more.

It could even be ability to do a task! Like a job for which one receives an income.

Anyway, the wee p-value applies just as equally and logically as to High/Low Bananas as races J/K. It must because the Bananas are just as true of the people in the sample as their race.

“C’mon Briggs, that’s absurd. Eating one more banana can’t cause discrimination in income.”

That’s probably true. But so what? Statistical models are silent on cause.

“No way. Everybody knows race is cause of discrimination. That p-value proved it.”

Everybody knows?

“They do.”

So why run the statistical model to “prove” what you already know? You’re arguing circularly. If statistical models show what-causes-what the wee p-value also proved that eating bananas causes income disparities.

Or, if you already knew race was a cause, then it is a cause regardless what the statistical model showed. If the p-value wasn’t wee, and race is a cause of income “disparity”, then race is still a cause of income “disparity.”

Using statistical models to “prove” discrimination is always cheap, lazy, and wrong. To really prove discrimination you have to do the hard work of investigating each person in the sample and discover what precisely caused his income. If you can’t do that, you can’t prove cause.

There’s much more to say on this dismal and rapidly expanding topic. If you want to defend yourself against spurious charges, don’t use statistics.

Still Trust Government & The News?


Two small items as I’m traveling today.

Destroying evidence works

DHS ordered me to scrub records of Muslims with terror ties

[In 2009] Twenty-three-year old Nigerian Muslim Umar Farouk Abdulmutallab intended to detonate Northwest Airlines Flight 253, but the explosives in his underwear malfunctioned…

Following the attempted attack, President Obama threw the intelligence community under the bus for its failure to “connect the dots.”…

Most Americans were unaware of the enormous damage to morale at the Department of Homeland Security, where I worked, his condemnation caused. His words infuriated many of us because we knew his administration had been engaged in a bureaucratic effort to destroy the raw material—the actual intelligence we had collected for years, and erase those dots…

After leaving my 15 year career at DHS, I can no longer be silent about the dangerous state of America’s counter-terror strategy, our leaders’ willingness to compromise the security of citizens for the ideological rigidity of political correctness–and, consequently, our vulnerability to devastating, mass-casualty attack.

…in early November 2009, I was ordered by my superiors at the Department of Homeland Security to delete or modify several hundred records of individuals tied to designated Islamist terror groups like Hamas from the important federal database, the Treasury Enforcement Communications System (TECS).

Fifteen points to the reader who understands that if the government deletes the evidence it has about terrorism, that same government will be able to say truthfully after the next attack, “We had no evidence that this attack was coming.” Plausible deniability.

Extra points to those who can say “Umar Farouk Abdulmutallab” three times fast.

Words for hire

Interesting thing was the above story appeared in a mainstream source, The Hill. Is it therefore suspicious? Russia Today is not a mainstream source, at least not here in the West. It is Russian mainstream.

Headline (my version): Journalist admits to lying.

Dr. Udo Ulfkotte is a top German journalist and editor and has been for more than two decades, so you can bet he knows a thing or two about mainstream media and what really happens behind the scenes. Recently, Dr. Ulfakatte went on public television stating that he was forced to publish the works of intelligence agents under his own name, also adding that noncompliance with these orders would result in him losing his job.

Ulfkotte (which is also easy to say fast) himself said:

I’ve been a journalist for about 25 years, and I was educated to lie, to betray, and not to tell the truth to the public. But seeing right now within the last months how the German and American media tries to bring war to the people in Europe, to bring war to Russia — this is a point of no return and I’m going to stand up and say it is not right what I have done in the past, to manipulate people, to make propaganda against Russia, and it is not right what my colleagues do and have done in the past because they are bribed to betray the people, not only in Germany, all over Europe.

There is a video of Ulfkotte with more information.

Believe what you like about it—I’m not defending or criticizing–but I’ll tell you this. I myself have been (recently) presented with op-eds, written by those who specialize in that kind of thing, that I was asked to sign as if I had written them. I am, of course, a relative nobody but I am a small authority in some areas.

I refused these commissions, and will always do so, but you have to wonder how common the practice is. I think we all know the answer. And I think we all know of the close ties the mainstream media has with the government.

The other odd thing is the increasing belligerent attitude we’re taking with Russia. But that’s a subject for another day.

On Twitter’s Shadow Banning


Friend of ours noticed late last week that his tweets were no longer showing up in Twitter searches. So I searched for his name, complete with Twitter handle, and saw that his tweets from the last two weeks weren’t showing up in searches, except those in exchanges with me, probably because we follow each other.

I conducted the same searches for others in our circle and found the censoring applied to the more prominent members, but not to all. One gentleman with over nine thousand followers (whom I’ll not name) had all his tweets undocumented; I mean, none of his tweets (over a period of years) come up in a search.

Now this could have been a hiccup in the system; perhaps Twitter’s search engine was lagging a few weeks behind. But the distinct correlation between the distance a person’s tweets departed the Overton Window and the delay in turning up said tweets in searches was suspicious.

Our friend meanwhile was actively tweeting about his possible shadow banning, and letting Twitter know he knew about it. Several others did the same.

Next day, all the searches were restored.

Shadowing banning? On social media, it’s when all appears normal to a user, but where that user’s content is in some way hidden from all the other users. In this case, it appears Twitter let users talk to each other but removed the rest of the community from searching the content of these users.

This isn’t the first time Twitter did something like this. A few weeks ago Twitter, in the weakest form of ban it has in its armamentarium, petulantly removed the verification badge from Breitbart’s Milo Yiannopoulos, who uses the handle @Nero. This move blew up in Twitter’s face and resulted in many new followers for Yiannopoulos and in worldwide negative publicity for Twitter. Twitter also removed, partially, the ability to easily search for the incident.

Twitter never explained and it never put Yiannopoulos’s badge back. Twitter has also outright banned, as in kicked off its system, many others. It doesn’t take a statistician to tell you the commonalities of most users cast into the wilderness.

Word is that Twitter will make the banning official “to tackle ‘trolling’ and ‘abuse’“. Which is strange for a system where everybody has to designate who they want to see, and where anybody can block anybody.

Well, now, what do you think of that?

It is, of course, Twitter’s perfect right to ban anybody they want, willy-nilly, or formally. It’s their playground and we pay nothing for it. So I’m not complaining, but I am noticing.

The first thing I notice is that Twitter wants to increase its followers, which they, and Wall Street, consider necessary to boost the stock price, which has fallen by half since the return of CEO Jack Dorsey. The executive staff has also fallen off, with a bevvy of top people bolting not too long after Dorsey’s return.

Still, the relevant question is: will Twitter gain followers from muting people on the right? The idea is to let people follow non-progressives, but to keep sensitive, triggerable eyes from accidentally discovering their opinions. That works, but the maneuver if applied widely tends to drive content towards the average, to the banal, to the same thing you can get anywhere. Why go to Twitter when it’s just an endless retweet of NPR stories?

Twitter regards right-wing thought as harmful to itself. Let that be so, and let them make it known to the world that “extremists” are no longer to be found on its system. Will those progressives and lefties who have been holding back in fear now throng to the site?

Consider too that as word gets out about these bans that people will be reluctant to try Twitter. And folks like me, who provide (so Twitter stats tell me) tens of thousands of impressions a day, might wrap it up and head off into the sunset.

Problem is, Twitter is too specialized, its content too transient. Twitter is like a cocktail party where the conversation never stops, and where you can pop in and take part, but where it’s difficult to discover what exactly was said before you go there. It’s only for the truly Internet-savvy. Casual users don’t get much out it. You also have to like to argue and pay attention, which isn’t most folks. So I think Twitter is already at its true maximum in terms of active users. Of course, any number of people can sign up, but if they don’t engage consistently, Twitter won’t realize any new substantial advertising dollars from whatever users it can coax out of the dark now that I and folks like me have been censored.

In other words, it’s time to short the stock.

UpdateFacebook is now removing speech that presumably almost everybody might decide is racist — along with speech that only someone at Facebook decides is ‘racist.’

In September, German Chancellor Angela Merkel met Mark Zuckerberg of Facebook at a UN development summit in New York. As they sat down, Chancellor Merkel’s microphone, still on, recorded Merkel asking Zuckerberg what could be done to stop anti-immigration postings being written on Facebook. She asked if it was something he was working on, and he assured her it was.

Update La la la.

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