Potpourri

24 January 2022. Curated ideas from past three days in January.

1. Watch this report from hard-nosed, “attack” journalism from 46 years ago and compare it to the lapdog mainstream media of today. A clear case of the CDC’s denial of the facts and of their responsibility, from Mike Wallace on 60 Minutes in 1976. The then head of the CDC acts like the inspiration of SNL’s John Lovett portrayal of a sweaty CEO pathological liar, the “that’s the ticket” skit.

2. Jesuit white privilege, income inequality and justice

· Highly paid, white administrators awarding full tuition scholarships to children of other highly paid, white administrators. Let’s face it. By today’s culture, merit scholarships are inherently racist violence against BIPOCs.

· Highly paid white administrators and their pay increases and promotions versus low-paid minority employees with little salary or career advancement.

· Taxing full tuition payers to provide aid to lower-income students.

· Jesuit sin, penance paid by other people. How is it just to ask someone else to pay for your sins? Why not just turn Georgetown over to the NAACP and make it a HBC?

o Per the Times: “In one of the largest efforts by an institution to atone for slavery, a prominent order of Catholic priests has vowed to raise $100 million to benefit the descendants of the enslaved people it once owned and to promote racial reconciliation initiatives across the United States.”

o And let’s acknowledge this: A religious order that consists primarily of white, western males and opposes a woman’s ‘right’ to an abortion is subject to cancellation at any moment. So this is as much a defensive strategy as an act of (other people’s) charity.

3. Tony Heller Rumble, “Climate Peter Principle”

IS NOT HONESTY THE WISEST POLICY?

MONTHLY WEATHER REVIEW

JANUARY 1907

It is wrong to mutilate or suppress the record of an observation of a phenomenon of nature, but it is also wrong to make a bad use of the record. In fact, it is the misuse of meteorological data, not the observing or publishing, that constitutes a crime against the community. Observation and careful research are to be encouraged as useful. Misrepresentations are to be avoided as harmful. The "Independent Press" as the " Voice of the People " should be not only " Vox Populi " but " Vox Dei ", repressing all cheats and hoaxes, defending the truth and the best interests of the whole nation as against the self-interest of a few.

4. Peter Temple: Global warming and the rise of great civilizations

The sun and the planets are the main drivers of climate, with warm cycles begetting great economic growth and the “dark ages” driven by global cooling.

5. Single woman after work in China, 2022

Lifestyle of the working Chinese single woman when she gets home from work: Gadgets make her lifestyle possible.

6. Data science, University of Chicago

Today’s lesson is all about regression to the mean—the statistical phenomenon that can make natural variation in data look like real change. Take an example just a few miles from the quads. Chicago White Sox catcher Yermín Mercedes began the 2021 season by getting eight hits in eight at-bats, posting an impossibly high 1.000 batting average. Baseball fans recognized this was an outlier; sure enough, over the season, Mercedes’s batting average regressed to the mean, dropping to a pedestrian .271. Outliers in a data set like this tend to revert to a value closer to the mean over time, regardless of whatever effect is being studied; a simplistic analysis misinterprets that as cause and effect. Regression to the mean trips up many researchers.

Biron cues up an example from a 1987 New York Times article touting the ability of a beta-blocker called propranolol to help overanxious students on their SATs. Twenty-five test-takers who scored lower than expected took the SAT again, but this time they were given propranolol. They performed an average of 120 points higher the second time. Sounds good? Not to Biron: “So, what might be wrong with the design of the study?”

A student points out it’s not a random sample and there’s no control group. She’s right on the money: “This is the worst thing you can do” in designing an experiment, says Biron. It’s easy to think of reasons other than anxiety that could have depressed the students’ original scores. Or it might be another example of regression to the mean. The point is, he explains, “with the original study design, you can’t prove anything.”

The next example was another drug study, this time on the bone density of patients taking one of two drug intended to prevent osteoporosis. Biron explains the data were measured at the start of the study, after 12 months, and after 24 months. People who lost bone density in the first 12 months of treatment seemed to recover it by month 24. But people who gained bone density in the first 12 months ended up losing it by the end.

If you only look at the data after 12 months, he says, you might conclude there’s a class of patients for whom the drugs were extremely effective, and another for whom they were worse than nothing. But the convergence of all the patients’ results by 24 months demonstrates regression to the mean. The outliers at 12 months—both positive and negative—were just chance. If the researchers had focused only on the patients who appeared to be responding to the drugs, Biron explains, they would have missed the regression to the mean of the other patients.

Biron closes by giving the students pointers on how to avoid bad data and flawed conclusions. Avoid preselecting data based on a cutoff; focusing on extreme data points that might regress to the mean later—as in the bone-density study—ends up biasing your conclusions. Randomly allocate subjects to trial and control groups as much as possible. (Remember the SAT study, bereft of a control group.) Take multiple baseline measurements to understand what natural variation exists before your study begins. (You’d want to know what role random chance plays in baseball statistics before declaring Yermín Mercedes the finest hitter ever, wouldn’t you?) Following this advice will minimize spurious effects in data analysis, he says: “Luck does not persist from trial to trial.”

7. Models behaving badly

‘Wildly incorrect’ Covid modelling bounced Boris Johnson into second lockdown, MPs told

Boris Johnson was bounced into the second coronavirus lockdown after a “terrifying” and “wildly incorrect” model warning of 4,000 deaths a day was leaked to the press, MPs have heard.

Mr. Seely warned that the “doomsday public health scenarios” had been used to create a “despicable” and “unforgivable” climate of fear, based on “a sort of glorified guesswork.”

“Never before has so much harm been done to so many, by so few based on so little questionable and potentially flawed data,” he said.

“We had a nervous Government presented with doomsday scenarios which panicked it into a course of profound acting with shocking outcomes.

"I believe the use of modelling is pretty much getting up there for a national scandal."

Mr. Seely told MPs that there was a growing body of evidence suggesting that the models were flawed, and the wrong assumptions made.

“Why did we think it was in our nation's interest to create a grotesque sense of fear to manipulate behaviour?”, he added.

“Never again should the Government rely on dubious modelling.”