Statistical Model Quotes

Essentially, all models are wrong, but some are useful

Indeed, statistical modelling based on these results even suggests that one of the effects of the plague was a substantial improvement in life expectancy.

In this country, it is not the highest virtue, nor the heroic act, that achieves fame, but the uncommon nature of the least significant destiny. There is plenty for everyone, then, since the more conformist the system as a whole becomes, the more millions of individuals there are who are set apart by some tiny peculiarity. The slightest vibration in a statistical model, the tiniest whim of a computer are enough to bathe some piece of abnormal behaviour, however banal, in a fleeting glow of fame.

It is critical to recognize the limitations of LLMs from a consumer perspective. LLMs only possess statistical knowledge about word patterns, not true comprehension of ideas, facts, or emotions. Their fluency can create an illusion of human-like understanding, but rigorous testing reveals brittleness. Just because a LLM can generate coherent text about medicine or law doesn’t mean it grasps those professional domains. It does not. Responsible evaluation is essential to avoid overestimating capabilities.

Models are the mothers of invention.

Newer systems use statistical machine learning techniques that automatically build statistical models from observed usage patterns.

Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.

You end up with a machine which knows that by its mildest estimate it must have terrible enemies all around and within it, but it can't find them. It therefore deduces that they are well-concealed and expert, likely professional agitators and terrorists. Thus, more stringent and probing methods are called for. Those who transgress in the slightest, or of whom even small suspicions are harboured, must be treated as terrible foes. A lot of rather ordinary people will get repeatedly investigated with increasing severity until the Government Machine either finds enemies or someone very high up indeed personally turns the tide... And these people under the microscope are in fact just taking up space in the machine's numerical model. In short, innocent people are treated as hellish fiends of ingenuity and bile because there's a gap in the numbers.

In high school algebra, someone had already worked out the formulas. The teacher knew them or could find them in the teacher’s manual for the textbook. Imagine a word problem where nobody knows how to turn it into a formula, where some of the information is redundant and should not be used, where crucial information is often missing, and where there is no similar example worked out earlier in the textbook. This is what happens when one tries to apply statistical models to real-life problems.

Most statistical models are built on the notion that there are independent variables and dependent variables, inputs and outputs, and they can be kept pretty much separate from one another.39 When it comes to the economy, they are all lumped together in one hot mess.

One reason why many statistical models are incomplete is that they do not specify the sources of randomness generating variability among agents, i.e., they do not specify why otherwise observationally identical people make different choices and have different outcomes given the same choice.

All models are wrong, but some are useful.’ CHAPTER 6 Algorithms, Analytics and Prediction

Sometimes the job of a data scientist is to know when you don't know enough.

You no longer watch TV, it is TV that watches you (live),” or again: “You are no longer listening to Don’t Panic, it is Don’t Panic that is listening to you”—a switch from the panoptic mechanism of surveillance (Discipline and Punish [Surveiller et punir]) to a system of deterrence, in which the distinction between the passive and the active is abolished. There is no longer any imperative of submission to the model, or to the gaze “YOU are the model!” “YOU are the majority!” Such is the watershed of a hyperreal sociality, in which the real is confused with the model, as in the statistical operation, or with the medium. …Such is the last stage of the social relation, ours, which is no longer one of persuasion (the classical age of propaganda, of ideology, of publicity, etc.) but one of deterrence: “YOU are information, you are the social, you are the event, you are involved, you have the word, etc.” An about-face through which it becomes impossible to locate one instance of the model, of power, of the gaze, of the medium itself, because you are always already on the other side.

This book is an essay in what is derogatorily called "literary economics," as opposed to mathematical economics, econometrics, or (embracing them both) the "new economic history." A man does what he can, and in the more elegant - one is tempted to say "fancier" - techniques I am, as one who received his formation in the 1930s, untutored. A colleague has offered to provide a mathematical model to decorate the work. It might be useful to some readers, but not to me. Catastrophe mathematics, dealing with such events as falling off a height, is a new branch of the discipline, I am told, which has yet to demonstrate its rigor or usefulness. I had better wait. Econometricians among my friends tell me that rare events such as panics cannot be dealt with by the normal techniques of regression, but have to be introduced exogenously as "dummy variables." The real choice open to me was whether to follow relatively simple statistical procedures, with an abundance of charts and tables, or not. In the event, I decided against it. For those who yearn for numbers, standard series on bank reserves, foreign trade, commodity prices, money supply, security prices, rate of interest, and the like are fairly readily available in the historical statistics.

Be wary, though, of the way news media use the word “significant,” because to statisticians it doesn’t mean “noteworthy.” In statistics, the word “significant” means that the results passed mathematical tests such as t-tests, chi-square tests, regression, and principal components analysis (there are hundreds). Statistical significance tests quantify how easily pure chance can explain the results. With a very large number of observations, even small differences that are trivial in magnitude can be beyond what our models of change and randomness can explain. These tests don’t know what’s noteworthy and what’s not—that’s a human judgment.

Because the decimation of the second, reborn Greenwood can also be laid at the feet of men and women who sat in air-conditioned offices and did their work with pencils and calculators, blue-line maps, real estate estimates, and government statistics. For the efforts to carve up the city's historic African American district had not ended with the attempted land grab for a new railroad terminal back in 1921. Now they had new names. Urban renewal. Redlining. Slum clearance. Model Cities. Opportunity. Progress.

Thus, they do not need to understand the statistical and mathematical models in depth. However, marketers need to understand the fundamental ideas behind a predictive model so that they can guide the technical teams to select data to use and which patterns to find.

These examples should be models for communication, precisely because they inspire curiosity. “How does money influence politics?” is not an especially engaging question, but “If I were running for president, how would I raise lots of money with few conditions and no scrutiny?” is much more intriguing.

The point that apocalyptic makes is not only that people who wear crowns and who claim to foster justice by the sword are not as strong as they think--true as that is: we still sing, 'O where are Kings and Empires now of old that went and came?' It is that people who bear crosses are working with the grain of the universe. One does not come to that belief by reducing social processes to mechanical and statistical models, nor by winning some of one's battles for the control of one's own corner of the fallen world. One comes to it by sharing the life of those who sing about the Resurrection of the slain Lamb.

Avoid succumbing to the gambler’s fallacy or the base rate fallacy. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation—you still need to rely on well-designed experiments to draw strong conclusions. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. The normal distribution is particularly useful in experimental analysis due to the central limit theorem. Recall that in a normal distribution, about 68 percent of values fall within one standard deviation, and 95 percent within two. Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an area.

The [Value at Risk model] was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.

There is no freedom or justice in exchanging the female role for the male role. There is, no doubt about it, equality. There is no freedom or justice in using male language, the language of your oppressor, to describe sexuality. There is no freedom or justice or even common sense in developing a male sexual sensibility—a sexual sensibility which is aggressive, competitive, objectifying, quantity oriented. There is only equality. To believe that freedom or justice for women, or for any individual woman, can be found in mimicry of male sexuality is to delude oneself and to contribute to the oppression of one’s sisters. Many of us would like to think that in the last four years, or ten years, we have reversed, or at least impeded, those habits and customs of the thousands of years which went before—the habits and customs of male dominance. There is no fact or figure to bear that out. You may feel better, or you may not, but statistics show that women are poorer than ever, that women are raped more and murdered more. I want to suggest to you that a commitment to sexual equality with males, that is, to uniform character as of motion or surface, is a commitment to becoming the rich instead of the poor, the rapist instead of the raped, the murderer instead of the murdered. I want to ask you to make a different commitment—a commitment to the abolition of poverty, rape, and murder; that is, a commitment to ending the system of oppression called patriarchy; to ending the male sexual model itself.

There are two primary strains in the Conservative Party: grocers, and grandees. … By ‘grandees’ and ‘grocers’, I am not referring to social class or any of that; nor do I refer to the Worshipful Company of Grocers, all cloves and camels. I refer rather to two fundamental positions within the Conservative Party, regardless of one’s antecedents. … A grandee Conservative sees the country as a village: a village of which he and his party, when in government, act the Squire. As the Squire, the grandee moves jovially amongst his tenants in their tied cottages, dispensing largesse and reproof…. There are two problems with this model. The first is that HMG is not the Squire and the subjects of the Crown are not the smocked tenantry of the government of the day. The second is that these principles – or instincts, as one can hardly call them principles – however different they may be to the fiercely held maxims of Labour old and new, lead in the end to the same statist solutions as those the Left proposes, and to accepting and ‘managing’ statism when a Conservative government succeeds a Labour one. It is the grocers who will always and rightly attempt to roll back the State and its reach in favour of liberty.

4.​They can cause a lot of damage to your body and your life. Because they’re frozen in dreadful scenes in the past and carry burdens from those times, they will do whatever they need to do to get your attention when you won’t listen: punish you or others, convince others to take care of them, sabotage your plans, or eliminate people in your life they see as a threat. To do these things and more, they can exacerbate or give you physical symptoms or diseases, nightmares and strange dreams, emotional outbursts, and chronic emotional states. Indeed, most of the syndromes that make up the Diagnostic and Statistical Manual are simply descriptions of the different clusters of protectors that dominate people after they’ve been traumatized. When you think of those diagnoses that way, you feel a lot less defective and a lot more empowered to help those protectors out of those roles.

A general challenge for the models we have written here, but for theory more generally in biology, is to be ahead of the experiments. Ultimately, we want to suggest exciting and revealing experiments that have not yet been conceived or undertaken. One of the critical frontiers in this area is to design experiments that showcase the uniquely nonequilibrium features of living systems, providing an impetus for new kinds of statistical physics.

I don’t mean to compare myself to a couple of artists I unreservedly admire—Miles Davis and Ray Charles—but I would like to think that some of the people who liked my book responded to it in a way similar to the way they respond when Miles and Ray are blowing. These artists, in their very different ways, sing a kind of universal blues, they speak of something far beyond their charts, graphs, statistics, they are telling us something about what it is like to be alive. It is not self-pity which one hears in them, but compassion. And perhaps this is the place for me to say that I really do not, at the very bottom of my own mind, compare myself to other writers. I think I really helplessly model myself on jazz musicians and try to write the way they sound. I am not an intellectual, not in the dreary sense that word is used today, and do not want to be: I am aiming at what Henry James called “perception at the pitch of passion.

System 1 is generally very good at what it does: its models of familiar situations are accurate, its short-term predictions are usually accurate as well, and its initial reactions to challenges are swift and generally appropriate. System 1 has biases, however, systematic errors that it is prone to make in specified circumstances. As we shall see, it sometimes answers easier questions than the one it was asked, and it has little understanding of logic and statistics. One further limitation of System 1 is that it cannot be turned off.

Buckminster Fuller often urged his audiences to try this simple experiment: stand, at "sunset," facing the sun for several minutes. As you watch the spectacular technicolor effects, keep reminding yourself, "The sun is not 'going down.’ The earth is rotating on its axis." If you are statistically normal, you will feel, after a few minutes, that, even though you understand the Copernican model intellectually, part of you — a large part — never felt it before. Part of you, hypnotized by metaphor, has always felt the pre-Copernican model of a stationary Earth.

It is a positive sign that a growing number of social movements are recognizing that indigenous self-determination must become the foundation for all our broader social justice mobilizing. Indigenous peoples are the most impacted by the pillage of lands, experience disproportionate poverty and homelessness, and overrepresented in statistics of missing an murdered women, and are the primary targets of repressive policing and prosecutions in the criminal injustice system. Rather than being treated as a single issue within a laundry list of demands, indigenous self-determination is increasingly understood as intertwined with struggles against racism, poverty, police violence, war and occupation, violence against women, and environmental justice. ... We have to be cautious to avoid replicating the state's assimilationist model of liberal pluralism, whereby indigenous identities are forced to fit within our existing groups and narratives. ... Indigenous struggle cannot simply be accommodated within other struggles; it demands solidarity on its own terms. Original blog post: Unsettling America: Decolonization in Theory and Practice. Quoted In: Decolonize Together: Moving beyond a Politics of Solidarity toward a Practice of Decolonization. Taking Sides.

Price mostly meanders around recent price until a big shift in opinion occurs, causing price to jump up or down. This is crudely modeled by quants using something called a jump-diffusion process model. Again, what does this have to do with an asset’s true intrinsic value? Not much. Fortunately, the value-focused investor doesn’t have to worry about these statistical methods and jargon. Stochastic calculus, information theory, GARCH variants, statistics, or time-series analysis is interesting if you’re into it, but for the value investor, it is mostly noise and not worth pursuing. The value investor needs to accept that often price can be wrong for long periods and occasionally offers interesting discounts to value.

If you can’t make a good prediction, it is very often harmful to pretend that you can. I suspect that epidemiologists, and others in the medical community, understand this because of their adherence to the Hippocratic oath. Primum non nocere: First, do no harm. Much of the most thoughtful work on the use and abuse of statistical models and the proper role of prediction comes from people in the medical profession.88 That is not to say there is nothing on the line when an economist makes a prediction, or a seismologist does. But because of medicine’s intimate connection with life and death, doctors tend to be appropriately cautious. In their field, stupid models kill people. It has a sobering effect. There is something more to be said, however, about Chip Macal’s idea of “modeling for insights.” The philosophy of this book is that prediction is as much a means as an end. Prediction serves a very central role in hypothesis testing, for instance, and therefore in all of science.89 As the statistician George E. P. Box wrote, “All models are wrong, but some models are useful.”90 What he meant by that is that all models are simplifications of the universe, as they must necessarily be. As another mathematician said, “The best model of a cat is a cat.”91 Everything else is leaving out some sort of detail. How pertinent that detail might be will depend on exactly what problem we’re trying to solve and on how precise an answer we require.

Marriage is inefficient!” she proclaims. “The whole construct is a model of wasted resources. The wife often stays home to care for the children, or even a single child, abandoning the career she worked so hard for, losing years of creative output. Beyond the wasting of talent, think of the physical waste. For every home, there are so many redundancies. How many toasters do you think there are in the world?” “I have no idea.” “Seriously, just guess.” “Ten million?” I say impatiently. “More than two hundred million! And how often do you think the average household uses its toaster?” Once again, she doesn’t wait for my answer. “Just 2.6 hours per year. Two hundred million toasters are sitting unused, statistically speaking, more than 99.97 percent of their active lives.

VaR has been called “potentially catastrophic,” “a fraud,” and many other things not fit for a family book about statistics like this one. In particular, the model has been blamed for the onset and severity of the financial crisis. The primary critique of VaR is that the underlying risks associated with financial markets are not as predictable as a coin flip or even a blind taste test between two beers. The false precision embedded in the models created a false sense of security. The VaR was like a faulty speedometer, which is arguably worse than no speedometer at all. If you place too much faith in the broken speedometer, you will be oblivious to other signs that your speed is unsafe. In contrast, if there is no speedometer at all, you have no choice but to look around for clues as to how fast you are really going.

Equally important, statistical systems require feedback—something to tell them when they’re off track. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes. Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive—and very common. If the people being evaluated are kept in the dark, the thinking goes, they’ll be less likely to attempt to game the system. Instead, they’ll simply have to work hard, follow the rules, and pray that the model registers and appreciates their efforts. But if the details are hidden, it’s also harder to question the score or to protest against it.

Modern statistics is built on the idea of models — probability models in particular. [...] The standard approach to any new problem is to identify the sources of variation, to describe those sources by probability distributions and then to use the model thus created to estimate, predict or test hypotheses about the undetermined parts of that model. […] A statistical model involves the identification of those elements of our problem which are subject to uncontrolled variation and a specification of that variation in terms of probability distributions. Therein lies the strength of the statistical approach and the source of many misunderstandings. Paradoxically, misunderstandings arise both from the lack of an adequate model and from over reliance on a model. […] At one level is the failure to recognise that there are many aspects of a model which cannot be tested empirically. At a higher level is the failure is to recognise that any model is, necessarily, an assumption in itself. The model is not the real world itself but a representation of that world as perceived by ourselves. This point is emphasised when, as may easily happen, two or more models make exactly the same predictions about the data. Even worse, two models may make predictions which are so close that no data we are ever likely to have can ever distinguish between them. […] All model-dependant inference is necessarily conditional on the model. This stricture needs, especially, to be borne in mind when using Bayesian methods. Such methods are totally model-dependent and thus all are vulnerable to this criticism. The problem can apparently be circumvented, of course, by embedding the model in a larger model in which any uncertainties are, themselves, expressed in probability distributions. However, in doing this we are embarking on a potentially infinite regress which quickly gets lost in a fog of uncertainty.

In 1963, the chaos theorist Edward Lorenz presented an often-referenced lecture entitled “Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” Lorenz’s main point was that chaotic mathematical functions are very sensitive to initial conditions. Slight differences in initial conditions can lead to dramatically different results after many iterations. Lorenz believed that this sensitivity to slight differences in the beginning made it impossible to determine an answer to his question. Underlying Lorenz’s lecture was the assumption of determinism, that each initial condition can theoretically be traced as a cause of a final effect. This idea, called the “Butterfly Effect,” has been taken by the popularizers of chaos theory as a deep and wise truth. However, there is no scientific proof that such a cause and effect exists. There are no well-established mathematical models of reality that suggest such an effect. It is a statement of faith. It has as much scientific validity as statements about demons or God.

Our exploration into advertising and media is at its root a critique of the exploitative nature of capitalism and consumerism. Our economic systems shape how we see our bodies and the bodies of others, and they ultimately inform what we are compelled to do and buy based on that reflection. Profit-greedy industries work with media outlets to offer us a distorted perception of ourselves and then use that distorted self-image to sell us remedies for the distortion. Consider that the female body type portrayed in advertising as the “ideal” is possessed naturally by only 5 percent of American women. Whereas the average U.S. woman is five feet four inches tall and weighs 140 pounds, the average U.S. model is five feet eleven and weighs 117. Now consider a People magazine survey which reported that 80 percent of women respondents said images of women on television and in the movies made them feel insecure. Together, those statistics and those survey results illustrate a regenerative market of people who feel deficient based on the images they encounter every day, seemingly perfectly matched with advertisers and manufacturers who have just the products to sell them (us) to fix those imagined deficiencies.18

This happens because data scientists all too often lose sight of the folks on the receiving end of the transaction. They certainly understand that a data-crunching program is bound to misinterpret people a certain percentage of “he time, putting them in the wrong groups and denying them a job or a chance at their dream house. But as a rule, the people running the WMDs don’t dwell on those errors. Their feedback is money, which is also their incentive. Their systems are engineered to gobble up more data and fine-tune their analytics so that more money will pour in. Investors, of course, feast on these returns and shower WMD companies with more money. And the victims? Well, an internal data scientist might say, no statistical system can be perfect. Those folks are collateral damage. And often, like Sarah Wysocki, they are deemed unworthy and expendable. Big Data has plenty of evangelists, but I’m not one of them. This book will focus sharply in the other direction, on the damage inflicted by WMDs and the injustice they perpetuate. We will explore harmful examples that affect people at critical life moments: going to college, borrowing money, getting sentenced to prison, or finding and holding a job. All of these life domains are increasingly controlled by secret models wielding arbitrary punishments.

In Bohr’s model of the atom, electrons could change their orbits (or, more precisely, their stable standing wave patterns) only by certain quantum leaps. De Broglie’s thesis helped explain this by conceiving of electrons not just as particles but also as waves. Those waves are strung out over the circular path around the nucleus. This works only if the circle accommodates a whole number—such as 2 or 3 or 4—of the particle’s wavelengths; it won’t neatly fit in the prescribed circle if there’s a fraction of a wavelength left over. De Broglie made three typed copies of his thesis and sent one to his adviser, Paul Langevin, who was Einstein’s friend (and Madame Curie’s). Langevin, somewhat baffled, asked for another copy to send along to Einstein, who praised the work effusively. It had, Einstein said, “lifted a corner of the great veil.” As de Broglie proudly noted, “This made Langevin accept my work.”47 Einstein made his own contribution when he received in June of that year a paper in English from a young physicist from India named Satyendra Nath Bose. It derived Planck’s blackbody radiation law by treating radiation as if it were a cloud of gas and then applying a statistical method of analyzing it. But there was a twist: Bose said that any two photons that had the same energy state were absolutely indistinguishable, in theory as well as fact, and should not be treated separately in the statistical calculations.

How I Got That Name Marilyn Chin an essay on assimilation I am Marilyn Mei Ling Chin Oh, how I love the resoluteness of that first person singular followed by that stalwart indicative of “be," without the uncertain i-n-g of “becoming.” Of course, the name had been changed somewhere between Angel Island and the sea, when my father the paperson in the late 1950s obsessed with a bombshell blond transliterated “Mei Ling” to “Marilyn.” And nobody dared question his initial impulse—for we all know lust drove men to greatness, not goodness, not decency. And there I was, a wayward pink baby, named after some tragic white woman swollen with gin and Nembutal. My mother couldn’t pronounce the “r.” She dubbed me “Numba one female offshoot” for brevity: henceforth, she will live and die in sublime ignorance, flanked by loving children and the “kitchen deity.” While my father dithers, a tomcat in Hong Kong trash— a gambler, a petty thug, who bought a chain of chopsuey joints in Piss River, Oregon, with bootlegged Gucci cash. Nobody dared question his integrity given his nice, devout daughters and his bright, industrious sons as if filial piety were the standard by which all earthly men are measured. * Oh, how trustworthy our daughters, how thrifty our sons! How we’ve managed to fool the experts in education, statistic and demography— We’re not very creative but not adverse to rote-learning. Indeed, they can use us. But the “Model Minority” is a tease. We know you are watching now, so we refuse to give you any! Oh, bamboo shoots, bamboo shoots! The further west we go, we’ll hit east; the deeper down we dig, we’ll find China. History has turned its stomach on a black polluted beach— where life doesn’t hinge on that red, red wheelbarrow, but whether or not our new lover in the final episode of “Santa Barbara” will lean over a scented candle and call us a “bitch.” Oh God, where have we gone wrong? We have no inner resources! * Then, one redolent spring morning the Great Patriarch Chin peered down from his kiosk in heaven and saw that his descendants were ugly. One had a squarish head and a nose without a bridge Another’s profile—long and knobbed as a gourd. A third, the sad, brutish one may never, never marry. And I, his least favorite— “not quite boiled, not quite cooked," a plump pomfret simmering in my juices— too listless to fight for my people’s destiny. “To kill without resistance is not slaughter” says the proverb. So, I wait for imminent death. The fact that this death is also metaphorical is testament to my lethargy. * So here lies Marilyn Mei Ling Chin, married once, twice to so-and-so, a Lee and a Wong, granddaughter of Jack “the patriarch” and the brooding Suilin Fong, daughter of the virtuous Yuet Kuen Wong and G.G. Chin the infamous, sister of a dozen, cousin of a million, survived by everbody and forgotten by all. She was neither black nor white, neither cherished nor vanquished, just another squatter in her own bamboo grove minding her poetry— when one day heaven was unmerciful, and a chasm opened where she stood. Like the jowls of a mighty white whale, or the jaws of a metaphysical Godzilla, it swallowed her whole. She did not flinch nor writhe, nor fret about the afterlife, but stayed! Solid as wood, happily a little gnawed, tattered, mesmerized by all that was lavished upon her and all that was taken away!

Though Hoover conceded that some might deem him a “fanatic,” he reacted with fury to any violations of the rules. In the spring of 1925, when White was still based in Houston, Hoover expressed outrage to him that several agents in the San Francisco field office were drinking liquor. He immediately fired these agents and ordered White—who, unlike his brother Doc and many of the other Cowboys, wasn’t much of a drinker—to inform all of his personnel that they would meet a similar fate if caught using intoxicants. He told White, “I believe that when a man becomes a part of the forces of this Bureau he must so conduct himself as to remove the slightest possibility of causing criticism or attack upon the Bureau.” The new policies, which were collected into a thick manual, the bible of Hoover’s bureau, went beyond codes of conduct. They dictated how agents gathered and processed information. In the past, agents had filed reports by phone or telegram, or by briefing a superior in person. As a result, critical information, including entire case files, was often lost. Before joining the Justice Department, Hoover had been a clerk at the Library of Congress—“ I’m sure he would be the Chief Librarian if he’d stayed with us,” a co-worker said—and Hoover had mastered how to classify reams of data using its Dewey decimal–like system. Hoover adopted a similar model, with its classifications and numbered subdivisions, to organize the bureau’s Central Files and General Indices. (Hoover’s “Personal File,” which included information that could be used to blackmail politicians, would be stored separately, in his secretary’s office.) Agents were now expected to standardize the way they filed their case reports, on single sheets of paper. This cut down not only on paperwork—another statistical measurement of efficiency—but also on the time it took for a prosecutor to assess whether a case should be pursued.

Was this luck, or was it more than that? Proving skill is difficult in venture investing because, as we have seen, it hinges on subjective judgment calls rather than objective or quantifiable metrics. If a distressed-debt hedge fund hires analysts and lawyers to scrutinize a bankrupt firm, it can learn precisely which bond is backed by which piece of collateral, and it can foresee how the bankruptcy judge is likely to rule; its profits are not lucky. Likewise, if an algorithmic hedge fund hires astrophysicists to look for patterns in markets, it may discover statistical signals that are reliably profitable. But when Perkins backed Tandem and Genentech, or when Valentine backed Atari, they could not muster the same certainty. They were investing in human founders with human combinations of brilliance and weakness. They were dealing with products and manufacturing processes that were untested and complex; they faced competitors whose behaviors could not be forecast; they were investing over long horizons. In consequence, quantifiable risks were multiplied by unquantifiable uncertainties; there were known unknowns and unknown unknowns; the bracing unpredictability of life could not be masked by neat financial models. Of course, in this environment, luck played its part. Kleiner Perkins lost money on six of the fourteen investments in its first fund. Its methods were not as fail-safe as Tandem’s computers. But Perkins and Valentine were not merely lucky. Just as Arthur Rock embraced methods and attitudes that put him ahead of ARD and the Small Business Investment Companies in the 1960s, so the leading figures of the 1970s had an edge over their competitors. Perkins and Valentine had been managers at leading Valley companies; they knew how to be hands-on; and their contributions to the success of their portfolio companies were obvious. It was Perkins who brought in the early consultants to eliminate the white-hot risks at Tandem, and Perkins who pressed Swanson to contract Genentech’s research out to existing laboratories. Similarly, it was Valentine who drove Atari to focus on Home Pong and to ally itself with Sears, and Valentine who arranged for Warner Communications to buy the company. Early risk elimination plus stage-by-stage financing worked wonders for all three companies. Skeptical observers have sometimes asked whether venture capitalists create innovation or whether they merely show up for it. In the case of Don Valentine and Tom Perkins, there was not much passive showing up. By force of character and intellect, they stamped their will on their portfolio companies.

The first thing to note about Korean industrial structure is the sheer concentration of Korean industry. Like other Asian economies, there are two levels of organization: individual firms and larger network organizations that unite disparate corporate entities. The Korean network organization is known as the chaebol, represented by the same two Chinese characters as the Japanese zaibatsu and patterned deliberately on the Japanese model. The size of individual Korean companies is not large by international standards. As of the mid-1980s, the Hyundai Motor Company, Korea’s largest automobile manufacturer, was only a thirtieth the size of General Motors, and the Samsung Electric Company was only a tenth the size of Japan’s Hitachi.1 However, these statistics understate their true economic clout because these businesses are linked to one another in very large network organizations. Virtually the whole of the large-business sector in Korea is part of a chaebol network: in 1988, forty-three chaebol (defined as conglomerates with assets in excess of 400 billion won, or US$500 million) brought together some 672 companies.2 If we measure industrial concentration by chaebol rather than individual firm, the figures are staggering: in 1984, the three largest chaebol alone (Samsung, Hyundai, and Lucky-Goldstar) produced 36 percent of Korea’s gross domestic product.3 Korean industry is more concentrated than that of Japan, particularly in the manufacturing sector; the three-firm concentration ratio for Korea in 1980 was 62.0 percent of all manufactured goods, compared to 56.3 percent for Japan.4 The degree of concentration of Korean industry grew throughout the postwar period, moreover, as the rate of chaebol growth substantially exceeded the rate of growth for the economy as a whole. For example, the twenty largest chaebol produced 21.8 percent of Korean gross domestic product in 1973, 28.9 percent in 1975, and 33.2 percent in 1978.5 The Japanese influence on Korean business organization has been enormous. Korea was an almost wholly agricultural society at the beginning of Japan’s colonial occupation in 1910, and the latter was responsible for creating much of the country’s early industrial infrastructure.6 Nearly 700,000 Japanese lived in Korea in 1940, and a similarly large number of Koreans lived in Japan as forced laborers. Some of the early Korean businesses got their start as colonial enterprises in the period of Japanese occupation.7 A good part of the two countries’ émigré populations were repatriated after the war, leading to a considerable exchange of knowledge and experience of business practices. The highly state-centered development strategies of President Park Chung Hee and others like him were formed as a result of his observation of Japanese industrial policy in Korea in the prewar period.

As the US Justice Department explains in their report on the Ferguson PD, “the lower rate at which officers find contraband when searching African-Americans indicates either that officers’ suspicion of criminal wrongdoing is less likely to be accurate when interacting with African-Americans or that officers are more likely to search African-Americans without any suspicion of criminal wrongdoing. Either explanation suggest bias, whether explicit or implicit” (US DOJ 2015, 65). Recent research by Stanford scientists suggests that this is also a problem in North Carolina (Simoiu, Corbett-Davies, and Goel 2017). The authors, using hierarchical statistical models that leverage geographic variation in stop outcomes, find that officers have a much lower search threshold when interacting with black and Hispanic motorists.

value, I can do three things,” he says. “I can improve the algorithm itself, make it more sophisticated. I can throw more and better data at the algorithm so that the existing code produces better results. And I can change the speed of experimentation to get more results faster. “We focused on data and speed, not on a better algorithm.” Candela describes this decision as “dramatic” and “hard.” Computer scientists, especially academic-minded ones, are rewarded for inventing new algorithms or improving existing ones. A better statistical model is the goal. Getting cited in a journal is validation. Wowing your peers gives you cred. It requires a shift in thinking to get those engineers to focus on business impact before optimal statistical model. He thinks many companies are making the mistake of structuring their efforts around building the best algorithms, or hiring developers who claim to have the best algorithms, because that’s how many AI developers think.

the global climate models (whether downscaled to regions or not) have failed to predict changes in the statistics of regional climate…

Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head—my mental models. None of these is or ever will be the real world. Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us. However, and conversely, our models fall far short of representing the world fully. That is why we make mistakes and why we are regularly surprised. In our heads, we can keep track of only a few variables at one time. We often draw illogical conclusions from accurate assumptions, or logical conclusions from inaccurate assumptions. Most of us, for instance, are surprised by the amount of growth an exponential process can generate. Few of us can intuit how to damp oscillations in a complex system.

Google was a company that’d made more money off advertisements than any other company in the history of the world, but it had been founded by people who were embarrassed by a business model dependent upon advertising lawn chairs, car insurance, and Viagra. To deflect the embarrassment, the company cloaked itself in an aura of innovation and some old bullshit about the expansion of human knowledge. Google maintained this façade by providing web and mobile services to the masses. The most beloved of these services was the near daily alteration of the company’s logo as it appeared on the company’s website. Almost every day, the Google logo transformed into cutesy, diminutive cartoons of people who’d done something with their lives other than sell advertisements. These cartoons were called Google Doodles. They encompassed the whole spectrum of achievement, with a special focus on scientific achievement and the lives of minorities. In its own way, this was a perfect distillation of politics in the San Francisco Bay Area. Whenever they appeared, the Google Doodles were beloved and celebrated in meaningless little articles on meaningless little websites. They were not met with the obvious emotion, which would be total fucking outrage at a massive multinational corporation co-opting a wide range of human experience into an advertisement for that very same corporation. Here was the perversity of Twenty-First-Century AD life: Native-American women had a statistically better chance of being caricatured in a Google Doodle than they did of being hired into a leadership position at Google. And no one cared. People were delighted! They were being honored! By a corporation!

If the prostitute of the eighteenth century was feeble-minded, lazy, false and mentally retarded, the 'sex worker' of today is described as independent, strong, truthful and liberated - everything her earlier version wasn't. She is not a woman to be pitied - she is a role model for us all. With this image as a security blanket, both the neoliberals and the postmodern leftists sleep well, without needing to consult the murder statistics.

it is from such diverse sources with varied networks and linkages that the response to HIV / AIDS has been patched together. it is an NGO model of response, uneven in coverage and quality, responsive to the particularities of local circumstance, the character of local leaders, and the availability and types of funds available.

Statistically speaking, you’re modeling the marriage your children will have.

Over the past few decades, advances in mathematical modeling and state-of-the-art statistical techniques have allowed scholars to examine the differences between democracy and dictatorship more carefully and systematically than ever before. The first generation of work using these tools seemed to corroborate the basic intuition that democracy is of the people, enacted by the people – and, crucially, for the people – in opposition to tyranny by one man or oligarchy by the few. But cracks have begun to emerge in the consensus that democracies are actually forged by the people and that their policies are intended to benefit the people

Compared to the human brain, machine learning isn’t especially efficient. A child places her finger on the stove, feels pain, and masters for the rest of her life the correlation between the hot metal and her throbbing hand. And she also picks up the word for it: burn. A machine learning program, by contrast, will often require millions or billions of data points to create its statistical models of cause and effect. But for the first time in history, those petabytes of data are now readily available, along with powerful computers to process them. And for many jobs, machine learning proves to be more flexible and nuanced than the traditional programs governed by rules.

Like the roulette wheel, a computer can produce a sequence of numbers that is random in the same sense. In fact, using a mathematical model, the computer could simulate the physics of the roulette wheel and throw a simulated ball at a slightly different angle each time in order to produce each number in the sequence. Even if the angles at which the computer throws the simulated ball follow a consistent pattern, the simulated dynamics of the wheel would transform these tiny differences into what amounts to an unpredictable sequence of numbers. Such a sequence of numbers is called a pseudorandom sequence, because it only appears random to an observer who does not know how it was computed. The sequence produced by a pseudorandom number generator can pass all normal statistical tests of randomness.

survey companies in Myanmar, with various respectable organizations arising as central members on the lookout. Among these organizations, Myanmar Study Exploration (MSR), Kantar TNS Myanmar, and Knowledge Myanmar Exploration stand apart as driving suppliers of statistical surveying and review administrations in the country. Myanmar Overview Exploration (MSR) has set up a good foundation for itself as an unmistakable review organization, offering a large number of examination and counseling administrations to homegrown and worldwide clients. With a solid spotlight on information exactness and unwavering quality, MSR has gained notoriety for conveying smart market knowledge and significant proposals to its clients. Kantar TNS Myanmar, one more key part in the overview business, brings an abundance of involvement and skill to the Myanmar market. As a component of the worldwide Kantar organization, the organization offers state of the art research strategies and a profound comprehension of buyer conduct, empowering clients to pursue informed choices and gain an upper hand in the commercial center. Knowledge Myanmar Exploration is likewise transforming the review business, giving top notch research arrangements custom fitted to the particular necessities of organizations working in Myanmar. The organization's obligation to conveying significant experiences and vital direction has added to its progress in serving a different cluster of clients across different areas. These study organizations assume a vital part in assisting organizations and associations with exploring Myanmar's dynamic market scene. By utilizing their skill in information assortment, examination, and translation, these organizations enable clients to acquire a more profound comprehension of customer inclinations, market patterns, and industry elements. Also, the presence of legitimate study organizations like MSR, Kantar TNS Myanmar, and Knowledge Myanmar Exploration mirrors the developing interest for solid and far reaching statistical surveying administrations in Myanmar. As the nation keeps on starting up to worldwide business open doors, the requirement for exact and noteworthy bits of knowledge has never been more prominent. As well as serving the necessities of organizations, these overview organizations likewise add to the improvement of survey companies in Myanmar overall. Through their obligation to maintaining elevated requirements of impressive skill and moral lead, they set a positive model for different players in the business and assist with raising the general nature of examination and counseling administrations accessible in the country. Besides, these organizations effectively draw in with neighborhood networks, giving work open doors and cultivating the improvement of nearby ability in the field of statistical surveying and information examination. By supporting a talented labor force and advancing information trade, they add to the structure of a vigorous and maintainable exploration biological system in Myanmar. All in all, the development of review organizations, for example, Myanmar Overview Exploration (MSR), Kantar TNS Myanmar, and Understanding Myanmar Exploration mirrors the rising significance of dependable statistical surveying and study administrations in Myanmar. With their obligation to greatness and their commitments to industry improvement, these organizations are ready to assume a critical part in forming the fate of survey companies in Myanmar.

The ‘quantitative revolution’ in geography required the discipline to adopt an explicitly scientific approach, including numerical and statistical methods, and mathematical modelling, so ‘numeracy’ became another necessary skill. Its immediate impact was greatest on human geography as physical geographers were already using these methods. A new lexicon encompassing the language of statistics and its array of techniques entered geography as a whole. Terms such as random sampling, correlation, regression, tests of statistical significance, probability, multivariate analysis, and simulation became part both of research and undergraduate teaching. Correlation and regression are procedures to measure the strength and form, respectively, of the relationships between two or more sets of variables. Significance tests measure the confidence that can be placed in those relationships. Multivariate methods enable the analysis of many variables or factors simultaneously – an appropriate approach for many complex geographical data sets. Simulation is often linked to probability and is a set of techniques capable of extrapolating or projecting future trends.

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The name overfitting comes from the way that statistical models are “fit” to match past observations. The fit can be too loose—this is called underfitting—in which case you will not be capturing as much of the signal as you could. Or it can be too tight—an overfit model—which means that you’re fitting the noise in the data rather than discovering its underlying structure.

The idea that society can be made more consistent, more accurate, and more fair by replacing idiosyncratic human judgment with numerical models is hardly a new one. In fact, their use even in criminal justice is nearly a century old.

Monte Carlo tree search makes predictions by generating examples (rollouts) and then makes a prediction based on the experience of this algorithm with these examples. Modern variations of Monte Carlo tree search further combine tree search with reinforcement learning and deep learning in order to improve predictions. Deep learning is used in order to learn the evaluation function instead of using domain knowledge. For example, AlphaZero uses Monte Carlo tree search for rollouts, while also using deep learning to evaluate the quality of the moves and guide the search. The Monte Carlo tree together with the deep learning models form the hypothesis used by the program in order to move moves. Note that these hypotheses are created using the statistical patterns of empirical behavior in games.

None of the reality-models discussed in this chapter, however bizarre they may seem to some readers, are any more arbitrary than the official reality-model known as consensus-reality, which is a statistical average and not nearly consensual as it seems. Travel 100 miles in any direction, and the consensus begins to crumble. Travel 1000 miles and very little consensus is left . . .

In contrast to this top-down, statist approach that forms obedient and willing subjects, the biblical model of peoplehood relies on individual actors collaborating for the good of the community.

In 1997, money manager David Leinweber wondered which statistics would have best predicted the performance of the U.S. stock market from 1981 through 1993. He sifted through thousands of publicly available numbers until he found one that had forecast U.S. stock returns with 75% accuracy: the total volume of butter produced each year in Bangladesh. Leinweber was able to improve the accuracy of his forecasting “model” by adding a couple of other variables, including the number of sheep in the United States. Abracadabra! He could now predict past stock returns with 99% accuracy. Leinweber meant his exercise as satire, but his point was serious: Financial marketers have such an immense volume of data to slice and dice that they can “prove” anything.

Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models.

You’ve already learned an effective way to do what performance reviews have been failing to do for decades. Using the Results Model regularly will meet these exact objectives without the ineffectiveness of reviewing a year’s worth of work through a triggering experience that statistically (and neurochemically) decreases performance.

Freud for fun, can design complex statistical models, speaks fluent Spanish, English, Italian, and Zapotec,

For the academics who currently populate top professional schools, design is a bit like shop class, akin to automobile repair or welding, and residing at a far remove from respectable activities like the mathematical modeling of stochastic processes and the statistical analysis of selection bias.

Everything we think and know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. None of these is or ever will be the real world.

Earth gravitates around the Sun, not the other way around (Galileo Galilei, 1614). Statistical models gravitate around data, not the other way around (Vincent Granville, 2014).

Econometrics is the application of classical statistical methods to economic and financial series. The essential tool of econometrics is multivariate linear regression, an 18th-century technology that was already mastered by Gauss before 1794. Standard econometric models do not learn. It is hard to believe that something as complex as 21st-century finance could be grasped by something as simple as inverting a covariance matrix.

We need a proper statistical model that lets each person have his own momentum effect and each person have his own checkout attraction and to see if we can pull him out from the data.

The social-personality approach to studying creativity focuses on personality and motivational variables as well as the socio-cultural environment as sources of creativity. Sternberg (2000) states that numerous studies conducted at the societal level indicate that “eminent levels of creativity over large spans of time are statistically linked to variables such as cultural diversity, war, availability of role models, availability of financial support, and competitors in a domain” (p. 9).

Machine learning tends to be more focused on developing efficient algorithms that scale to large data in order to optimize the predictive model. Statistics generally pays more attention to the probabilistic theory and underlying structure of the model.

I am a reporter on climate change. I have been following the topic for New Scientist magazine in the UK and others for twenty years now. And when I talk to climate scientists during their coffee breaks and at their private conferences—as I have done extensively both before and after completing this book—I hear them warn that the current accepted predictions could be much too optimistic; that their statistical models of climate, sophisticated though they undoubtedly are, badly underestimate the forces of change; that we could be close to triggering sudden lurches in the world’s climate. Hence the subtitle of the book: Why Scientists Fear Tipping Points in Climate Change.

This is a very important distinction between weather and climate models: for climate forecasts, the initial conditions in the atmosphere are not as important as the external forcings that have the ability to alter the character and types of weather (i.e., the statistics or what scientists would call the “distribution” of the weather) that make up the climate.

statistician William Sanders in Tennessee, who began his career advising agricultural and manufacturing industries. Sanders claimed that his statistical modeling could determine how much “value” a teacher added to her students’ testing performance.

Today we aren’t quite to the place that H. G. Wells predicted years ago, but society is getting closer out of necessity. Global businesses and organizations are being forced to use statistical analysis and data mining applications in a format that combines art and science–intuition and expertise in collecting and understanding data in order to make accurate models that realistically predict the future that lead to informed strategic decisions thus allowing correct actions ensuring success, before it is too late . . . today, numeracy is as essential as literacy. As John Elder likes to say: ‘Go data mining!’ It really does save enormous time and money. For those

More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills — skills that are also necessary for understanding biases in the data, and for debugging logging output from code. Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms — some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.

David Viniar, CFO of Goldman Sachs, claimed as the global financial crisis broke in August 2007 that his bank had experienced ‘25 standard deviation events’ several days in a row. But anyone with a knowledge of statistics (a group that must be presumed to include Viniar) knows that the occurrence of several ‘25 standard-deviation events’ within a short time is impossible. What he meant to say was that the company’s risk models failed to describe what had happened. Extreme observations are generally the product of ‘off-model’ events. If you toss a coin a hundred times and all the tosses are heads, you may have encountered a once in a lifetime statistical freak; but look first for a simpler explanation. For all their superficial sophistication, the masters of the universe had no real understanding of what was going on before them.

The world `out there' is an exceedingly complicated mass of sensations, events, and turmoil. With Thomas Kuhn, I do not believe that the human mind is capable of organizing a structure of ideas that can come even close to describing what is really out there. Any attempt to do so contains fundamental faults. Eventually, those faults will become so obvious that the scientific model must be continuously modified and eventually discarded in favor of a more subtle one. We can expect the statistical revolution will eventually run its course and be replaced by something else.

CAN STATISTICAL MODELS BE USED TO MAKE DECISIONS? WHAT IS THE MEANING OF PROBABILITY WHEN APPLIED TO REAL LIFE? DO PEOPLE REALLY UNDERSTAND PROBABILITY? IS PROBABILITY REALLY NECESSARY? WHAT WILL HAPPEN NEXT

And the rest of us? We should grasp the basics of math and statistics-certainly better than most of us do today-but still follow what we love. The world doesn't need millions of mediocre mathematicians, and there's plenty of opportunity for specialists in other fields. Even in the heart of opportunity for specialists in other fields. Even in the heart of the math economy, at IBM Research, geometers and engineers work on teams with linguists and anthropologists and cognitive psychologists. They detail the behavior of humans to those who are trying to build mathematical models of it. All of these ventures, from Samer Takriti's gang at IBM to the secretive researchers laboring behind the barricades at the National Security Agency, feed from the knowledge and smarts of diverse groups. The key to finding a place on such world-class teams is not necessarily to become a math whiz but to become a whiz at something. And that something should be in an area that sparks the most enthusiasm and creativity within each of us. Somewhere on those teams, of course, whether it's in advertising, publishing, counterterrorism, or medical research, there will be at least a few Numerati. They'll be the ones distilling this knowledge into numbers and symbols and feeding them to their powerful tools.

Housing prices had never before fallen as far and as fast as they did beginning in 2007. But that’s what happened. Former Federal Reserve chairman Alan Greenspan explained to a congressional committee after the fact, “The whole intellectual edifice, however, collapsed in the summer of [2007] because the data input into the risk management models generally covered only the past two decades, a period of euphoria. Had instead the models been fitted more appropriately to historic periods of stress, capital requirements would have been much higher and the financial world would be in far better shape, in my judgment.”3

No scientist is as model minded as is the statistician; in no other branch of science is the word model as often and consciously used as in statistics.

Any subject whose history ranges from pump handles on London's Broad Street, tide tables, naval gunfire and models of social segregation is bound to have rich parentage. It took 'a village' to beget computational epidemiology: as a true multi-disciplinary subject, it evolved at the crossroads of mathematics, computation, statistics and medicine, with some contributions from systems biology, virology, microbiology, game theory, geography and perhaps even the social sciences.

There is an unhelpful tendency to regard superspreaders – and events where superspreading has occurred – as anomalies out of the ordinary. This contributes relatively little to our understanding of infectious dynamics and is bound to exacerbate the stigmatisation of individuals, as it has e.g. during the early years of AIDS, when much sensationalistic and unjustified blame was laid at the feet of early HIV patient Gaetan Dugas (on which see McKay, 2014). Rather, superspreading is one 'tail' of a distribution prominent mainly because it is noticeable – statistical models predict that there are generally an equal number of 'greatly inferior spreaders' who are particularly ineffective in spreading the illness.

Second, even if the underlying data could accurately predict future risk, the 99 percent assurance offered by the VaR model was dangerously useless, because it’s the 1 percent that is going to really mess you up.

In fact, the models had nothing to say about how bad that 1 percent scenario might turn out to be.

As with the “You can prove anything with statistics” claim, I usually find that the people making these other irrational claims don’t even quite mean what they say, and their own choices will betray their stated beliefs. If you ask someone to enter a betting pool to guess the outcome of the number of heads in 12 coin tosses, even the person who claims odds can’t be assigned will prefer the numbers around or near six heads. The person who claims to accept no risk at all will still fly to Moscow using Aeroflot (an airline with a safety record worse than any U.S. carrier) to pick up a $1 million prize. In response to the skeptics of statistical models he met in his own profession, Paul Meehl proposed a variation on the game of Russian roulette.15 In his modified version there are two revolvers: one with one bullet and five empty chambers and one with five bullets and one empty chamber. Meehl then asks us to imagine that he is a “sadistic decision-theorist” running experiments in a detention camp. Meehl asks, “Which revolver would you choose under these circumstances? Whatever may be the detailed, rigorous, logical reconstruction of your reasoning processes, can you honestly say that you would let me pick the gun or that you would flip a coin to decide between them? Meehl summarized the responses: “I have asked quite a few persons this question, and I have not yet encountered anybody who alleged that he would just as soon play his single game of Russian roulette with the five-shell weapon.” Clearly, those who answered Meehl’s question didn’t really think probabilities were meaningless. As we shall see before the end of this chapter, Meehl’s hypothetical game is less “hypothetical” than you might think.

There are lies, damned lies - statistics and then computer models.

Once we account for Christian nationalism in our statistical models, white Americans who attend church more often, pray more often and consider religion more important are less likely to prioritize the economy or liberty over the vulnerable.

1. Data show the CO2 level rose to 410 ppm by 2020, an increase of 130 ppm. 2. The IPCC assumes its core theory is true, which forces the conclusion that human CO2 caused all the increase above 280 ppm. 3. IPCC agrees that human CO2 emissions are less than 5 percent of natural CO2 emissions. 4. How can less that 5 percent of all CO2 emissions cause 32 percent of the CO2 in the atmosphere? Answer: It can’t. 8.2 Multiple lines of evidence prove IPCC’s core theory is wrong. 1. Ice core data prove natural CO2 caused the CO2 increase. 2. Direct CO2 data prove CO2 was much higher than 280 ppm before 1750. 3. Leaf stomata data prove CO2 was much higher than 280 ppm before 1750. 4. Statistics prove human CO2 is not the primary cause of the increase in CO2. 5. IPCC’s human carbon cycle is not consistent with its own natural carbon cycle. This is a basic physics error. 6. Inspection shows IPCC’s human carbon cycle is based on IPCC’s invalid assumption that its core theory is true. 8.3 A simple physics carbon cycle model replicates IPCC’s data for its natural carbon cycle. 1. This model easily calculates the true human carbon cycle that is compatible with IPCC’s natural carbon cycle. 2. The true human carbon cycle shows human CO2 has

The AI brain model is derived from the quad abstract golden ratio sΦrt trigonometry, algebra, geometry, statistics and built by adding aspects and/or characteristics from the diablo videogame. The 1111>11>1 was then abstracted from the ground up in knowing useful terminology in coding, knowledge management, and an ancient romantic dungeon crawler hack and slash games with both male and female classed and Items. I found the runes and certain items in the game to be very useful in this derivation, and I had an Ice orb from an Oculus of a blast doing it through my continued studies on decimal to hexadecimal to binary conversions and/or bit shifts and rotations from little to big endian. I chose to derive from diabo for two major reasons. The names or references to the class's abilities with unique, set, rare items were out of this world, and I sort of found it hard to believe that they had the time and money to build them. Finally, I realized my objective was complete when I realized that I created the perfect AI brain with Cognitive, Affective, and Psychomotor skills...So this is It? I'm thinking wow!

The AI brain model is derived from the quad abstract golden ratio, sΦrt, trigonometry, algebra, geometry, statistics, and built by adding aspects and/or characteristics from the diablo videogame. The 1111>11>1 was then abstracted from the ground up in knowing useful terminology in coding, knowledge management, and an ancient romantic dungeon crawler hack and slash game with both male and female classes and Items. I found the runes and certain items in the game to be very useful in this derivation, and I had an Ice orb from an Oculus of a blast in time doing it through my continued studies on decimal to hexadecimal to binary conversions and/or bit shifts and rotations from little to big endian. I chose to derive from diabo for two major reasons. The names or references to the class's abilities with unique, set, and rare items were out of this world, and I sort of found it hard to believe that they had the time and money to build it from in USA companies. Finally, I realized my objective was complete that I created the perfect AI brain with Cognitive, Affective, and Psychomotor skills...So this is It? I'm thinking wow!

The AI brain model is derived from quad abstract, golden ratio, sΦrt, trigonometry, algebra, geometry, statistics, and built by adding aspects and/or characteristics from the diablo videogame. The 1111>11>1 was then abstracted from the ground up in knowing useful terminology in coding, knowledge management, and an ancient romantic dungeon crawler hack and slash game with both male and female classes and Items. I found the runes and certain items in the game to be very useful in this derivation, and I had an Ice orb from an Oculus and a blast from the past doing it through my continued studies on decimal to hexadecimal to binary conversions and/or bit shifts and rotations from little to big endian. I chose to derive from diablo for two major reasons. The names or references to the class abilities with unique, set, and rare items were out of this world, and I sort of found it hard to believe that they had the time and money to build it from Inna USA company. Finally, I realized my objective was complete when I created the perfect AI brain with Cognitive, Affective, and Psychomotor skills...So this is It? I'm thinking wow!

Gosset examined the data and determined that the counts of yeast cells could be modeled with a probability distribution known as the “Poisson distribution.

One of the reasons why a lot of the statistical claims about COVID-19 are controversial is that endogeneity problems abound. Early on in the pandemic, it was worryingly common, for example, to hear people take the numbers of deaths within some age brackets from COVID-19, divide that by the total population of the group, and then use the low number to conclude that people in that group are less likely to die from COVID-19 than from being struck by lightning, being a victim of a shark attack, or something else that seems a relatively tiny risk. The implicit mental model here is that becoming infected with COVID-19 is a matter of random chance and therefore the death rates observed so far represent an accurate representation of the risk of getting and dying from the disease. But this is obviously not true. Your chance of getting infected and dying of COVID-19 is influenced by both your behavior and policy. That there have been relatively higher infections and deaths in prisons or meatpacking plants does not necessarily tell us that prisoners or meatpacking workers have personal characteristics that make them more susceptible to the worst outcomes from the disease. It might simply be that they spend much time in a place that puts them more at risk of infection.

Because the Church is mystery, there can be no question of deductive or crudely empirical tests. Deduction is ruled out because we have no clear abstract concepts of the Church that could furnish terms for a syllogism. Empirical tests are inadequate because visible results and statistics will never by themselves tell us whether a given decision was right or wrong.

As we saw, machine learning algorithms optimizing solely for predictive accuracy may discriminate against racial or gender groups, while algorithms computing aggregate statistics or models from behavioral or medical data may leak compromising information about specific individuals.