Statistics

Fight Of The Century! Ethan “The Scidolator” Siegel vs. Sam “Reality Boy” Bowman

In the near corner, we have Ethan “The Scidolator” Siegel, a record of 0-32, 132 pounds (I’m guessing), southpaw with a slight case of carpal tunnel syndrome, wearing red and yellow, held up with decaying elastic.

In the far corner stands Sam “Reality Boy” Bowman, a new fighter, 158 pounds, ambidextrous, with a pen that shows promise. Bowman is in all white. Rumor is he only took this match to grab some experience points.

Both fighters check their Twitter notifications. The crowd has fallen asleep. The bell rang almost a minute ago. And just like that…

Siegel has thrown the first punch!You Must Not ‘Do Your Own Research’ When It Comes To Science“. Bowman saw this coming since yesterday, and brushes it aside with “The four sins of science — and how to overcome them“.

Siegel jabs with his left: “If you ‘do your own research,’ you can no doubt find innumerable websites, social media accounts, and even a handful of medical professionals who are sharing opinions that confirm whatever your preconceived notions about COVID-19 are. However, do not fool yourself: you are not doing research.”

This falls so far short, Bowman thinks it’s a trick. So he starts paraphrasing quotations from Stuart Ritchie’s Science Fictions: Exposing Fraud, Bias, Negligence and Hype in Science. He starts with an probing jab:

A so-called ‘replication crisis’ has run through the [scientific field of psychology], with psychologists unable to repeat the results of key experiments, including a famous one on the ‘priming effect’, where researchers were apparently able to influence people’s behaviour simply by showing them the right words.

This takes Siegel aback. He thought he’d be assailed first with Bowman’s credentials. He just wasn’t ready for Bowman’s quick one-two.

The fact that behavioural economics sits on a throne of lies — weak, unreplicable or even downright fraudulent research — has not yet reached many policymakers, who still think of things like priming effects and ‘choice architecture’ as the hottest show in town.

A lightning slash! It sends Siegel reeling into the ropes! But he’s a veteran. He shakes it off. He fakes with his right, threatens a Fauci quote, and lets go with his left:

There is no excuse, with all the wonderful scientists and science communicators telling the truth about a whole slew of issues in our world, for people to seek out only the opinions that confirm their own biases. The best scientists in the world — even the ones who hold contrarian beliefs of their own — all agree that we should base our policies on the scientific consensus that we’ve achieved.

The blow puzzles Bowman. He didn’t expect the “wonderful scientists.” Angered, he lets go with a solid roundhouse right:

Negligence with things like data and samples has led errors to pervade entire fields. One example is the study of cell lines, cultures of animal cells that can be used in place of primary cells for biological and medical research, which are essentially immortal and can thus be the basis for experiments over long periods of time. Here, mislabeled samples and cluttered labs have led to thousands of errors, with scientists thinking they were working with, say, human bone cancer cells when they were actually working with cells from a pigs’ colon

Siegel isn’t fazed. He’s had worse and stayed on his feet. He lashes out with a series of quick jabs: “It’s absolutely foolish to think that you, a non-expert who lacks the very scientific expertise necessary to evaluate the claims of experts, are going to do a better job than the actual, bona fide experts of separating truth from fiction or fraud.” Wham! “The consequences of getting it wrong can lead to permanent consequences and may even be a life-or-death matter for many.” Pow!

The death blow drew blood. The crowd oohs, and even aahs. Siegel smiles.

This was his undoing.

He turns to wink at the press in the box and doesn’t even see Bowman’s powerhouse right cross.

The most surprising thing about Ritchie’s fourth sin, hype, is that it is the scientists themselves — not journalists — who are often responsible. These scientists are frequently heavily involved in writing the press releases about their own papers, and researchers have found that if the press release hyped up a finding there was a much greater chance of the media exaggerating them too.

For a second, Bowman is confused. Siegel smiles again, seemingly unfazed. He turns to salute the crowd—and falls flat on his face.

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Categories: Statistics

19 replies »

  1. I want a study on whether the non-scientist or the “expert” knows better. My bet is you take away all the paperwork, computer files, etc and run the “expert” against the dedicated citizen scientist just from memory and retained knowledge, the expert looks like Obama in drag. Not a pretty picture. Experts are experts because they have letters after their name. No other reason. I learned this with computers and started refusing to pay anyone to fix my computer if they showed up and knew less about the computer than I did. Experts are the tiny man behind the curtain and should be called out on it all the time. Some, like Fauci and Mann, should be jailed for malpractice. A title does not indicate knowledge, rather the money and/or time and power to get the paper. People with the paper and the letters after their name HATE the reality of this, but it is demonstrably true. As I learned in logic and philosophy, only the data matters, NOT WHO SAID IT. That concept is not really accepted by most people. Tiny, foolish, ignorant people believe experts and refuse to think on their own. Those who do think on their own frighten these creatures. Always have, always will. Perhaps it’s best. It keeps humans fearful, stupid and in bondage, a condition they seem to enjoy. (And, of course, leads to the apocalyptic outcomes displayed in science fiction, NEVER the stupid, happy, perfect Star Trek crap.)

  2. The average scientist is a button counting, bottle washing midwit. They’ve been carefully trained to think in only one way, and to not question their educators or look at primary data. Then, in graduate school, they are taught that money is King, and all studies shall be conducted in such a way as to continue the revenue stream. As postdoctoral researchers, they sell their souls to the grant foundations and government teat.

    Prime example – How much money and time is currently being wasted on “quantum computers” that are not any better (and normally much, much worse) than hydraulic analog computers?

  3. “Don’t blame journalists for overhyping science – it’s often the fault of scientists themselves”

    The more top journalist hang the better.

  4. @Sheri-

    I call it the paradox of expertise. Experts are more error-prone than non-experts, primarily for two reasons.

    First, experts have more opportunities to make mistakes than non-experts. I’m a master tradesman in a construction field, and I’ve made far more major messes than the average homeowner, but mostly because I only my trade every day and the average homeowner doesn’t. I also tend focus on far more difficult aspects of my trade, while DIYers tend to just attempt the easy stuff. As such, I’ve made way more mistakes because I tackle more difficult things more frequently.

    Second, experts are more likely to make mistakes because they tend to evaluate more variables. This is counterintuitive, but the basic issue is that while an expert is more likely to correctly evaluate each variable in his analysis, there comes a point where including more variables decreases the total probability of getting the analysis correct. (Mathematically, someone analyzing two variables with a 20% chance each of incorrectly analyzing them is more likely to provide a totally correct analysis than someone analyzing nine variables with a 5% chance of incorrectly analyzing each variable. Basically, the more you attempt account for the higher the odds that you make one miscalculation, and therefore the higher the odds that you are wrong.) There’s a reason the human mind is geared to simple models; and a certain point complexity becomes a liability instead of an asset, and it tends to hit that point quickly.

  5. Dean: I’m sticking with my statement. Even if the speaker is what appears to be a total whack job in a princess dress is the speaker. Even if it’s Alex Jones or David Icke. Certain speakers cry out to make sure one does one’s due diligence is at top level, of course. (I want to thank you for that image that I will have stuck in my head now. ? )

  6. Briggs can write with the best.

    An ability to write incisive, readable and clear prose is frequently a good indicator of rationality and competence (see “Buffett, Warren E.”).

    Bravo, Briggs, bravo !!

  7. It’s really very simple….and just another form of Kafkatrapping….

    If you do research that supports your own, contrarian (meaning minority) perspective, then that can’t be trusted because you can’t be trusted because you don’t have the right kind of White Lab Coat…and we can only assume that whatever conclusions are reached are thereby wrong.

    This means of course that you must adhere to orthodox (meaning majority) perspectives, which of course can be trusted because … the majority wears the right kind of White Lab Coat (and you don’t).

    Or — said more simply: We are right. If you agree with us then you’re right too.

    If you disagree you’re wrong. If you cite disagreeing research, that’s wrong…and you’re doubly wrong for citing disagreeing research that you couldn’t possibly have understood. And we know you didn’t understand it because you didn’t see how wrong it is. The only possible way for you to be right is to agree with us and our White Coats. Your disagreement only demonstrates how horribly incapable you are of understanding what is Right.

  8. Speaking of faith in experts, a lot of sites are touting the new IHME projections which say that we could have over 400,000 dead by the end of the year in the US due to COVID-19.

    Does anyone know how their model actually works? From what is available on their site, there are all sorts of weird things going on with the data. It is common to have sudden spikes in the projections with no clear rhyme or reason. For example, if you go to the New York State data, there is a measure of “mobility” (meaning how much people travel I guess). In all three of their scenarios it steadily rises, except for the “current projection” (meaning projecting the results of keeping the current lockdown measures in place) where the mobility score drops from -10% on December 12 and then goes to -66% on December 13, where it thereafter stays.

    This is definitely being carried through in other calculations, because there is a sharp drop in estimated infections on the same day and a sharp drop in estimated deaths 14 days later. In fact, the same phenomenon happens in other states, but on different days. In Minnesota the drop is on October 24, in California on November 12, in Ohio on November 25, in Tennessee on October 20 and so on.

    Interestingly this results in many states having lower daily deaths at the end of the year under the “current projection” scenario (i.e. lockdown measures maintained) than the “masks” scenario (described as current situation plus 95% mask usage).

  9. Digging into the data some more, I may have a partial answer.

    First the assumption used in the data, based off chart headings and the FAQ is that anyone wearing a mask will spread the disease at a rate 30% less than normal. Mask usage is assumed to remain constant in all scenarios except the “mask” scenario, in which it increases at a linear rate from its current level to 95% usage over the course of the next seven days.

    From looking at the data, it seems that infections are model purely as a rate of increase based off the current “mobility” level. Mobility itself seems to just slowly increase in the absence of new mandates, unaffected by things like weather or holidays. The only thing that can decrease it in the model are new mandates, which happen when in everything but the “easing” scenario. These occur whenever the number of daily deaths exceeds .0008% of the total population of the region. These cause mobility to instantaneously decrease drastically for six weeks, after which it gradually increases at the previous rate.

    Amusingly, the FAQ even mentions that this behavior can cause the “mask” scenario to result in more deaths in certain circumstances, because the reduced transmissions can lead to the deaths staying just below .0008% for a long period of time whereas with faster transmission the threshold is reached faster causing a decrease in mobility which has more of an effect on the model than masks do. So it’s not a lie to say that the IHME says that wearing a mask can result in more people dying.

    I’m not sure how the deaths and infections are related. From the data it looks like deaths simply are calculated as a percentage of infections about two weeks prior. But the FAQ says that they calculate infections by starting with the number of deaths and then working backwards. Maybe that applies only to the historical data, maybe it’s used in the projections too. I don’t know.

    These are all just observations from what is on the IHME site and what I can see in the behavior of the data. I don’t know if they have published the actual model they used in an accessible location.

  10. I did manage to find the relevant papers (here: http://www.healthdata.org/covid/publications) but they do not appear to have information on the current 3 scenario mobility based model being displayed on the website.

    I do note that all the papers which have been made available are preprints and (as MedRxiv warns) are not peer reviewed. Does this mean that in the eyes of scidolators that they do not represent the “consensus of the scientific community” and thus must be discarded?

  11. Briggs ==> Ethan Siegel, like many who say “listen only to us experts”, uses a misleading graph for “the Earth is warming”. The graph he shows is for “Land and Sea Temperature Anomalies” for “January and December” — which does not necessarily support his claim. If he had claimed that the “air and oceans” have been warming a little bit during the Northern Hemisphere winter since 1970 or so he would have not committed this misdemeanor.

    Like Covid-19 deaths, it is always important to pay close attention to “What Are They Really Counting?”.

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