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Category: Statistics

The general theory, methods, and philosophy of the Science of Guessing What Is.

March 24, 2018 | 6 Comments

Insanity & Doom Update XXVII

Item Homeschooling Parents Would Rather ‘Go to Jail’ Than Send Daughter to State School

The parents of a homeschooled child have said they would be willing to go to prison rather than submit to Westminster Council’s demand to interview their daughter or send her to a state-run school.
Edward Hardy and his wife Eileen Tracy, both education professionals, were told that the family must meet with a home education advisor, submit an “endorsement by an educator”, or send their 12-year-old daughter Lilian to school by March 7th, reports The Telegraph.

The parents declined the inspection, and offered a sample of Lilian’s work, feeling that should be enough as the Department of Education’s guidelines state that “Local authorities have no statutory duties in relation to monitoring the quality of home education on a routine basis”.

The State is a jealous god.

Item Britain’s first university well-being tsar hands out ‘happy badges’ to students

Students feeling anxious in the approach to exam season will be given “happy badges” by Britain’s first university “happiness tsar”.

The University of Buckingham said the system will be led by Cherry Coombe, whose role is to “help improve the working and learning environment” by promoting mental well-being.

The badges, which look like traditional prefect badges but have the word “happiness” written in gold, have so far been awarded to students who took part in a local parade through Buckingham and to staff whose actions “improved the work environment”.

The parade float, put together by students, was themed on the BBC drama Doctor Foster, and saw students dress in scrubs. Ms Coombe said that the badges were awarded to students to “formalise the notion of being successful as a resilient person”.

Formalise. Say it slow. Formalise.

Item College Places HCFA On ‘Probation’ After Group Barred Student in Same-Sex Relationship from Leadership

The Office of Student Life has placed religious group Harvard College Faith and Action on “administrative probation” for a year after the organization pressured a female member of its student leadership to resign in September following her decision to date a woman.

College spokesperson Aaron M. Goldman announced the move to put HCFA on probation in an emailed statement sent to The Crimson Wednesday afternoon.

“After a thorough review and finding that HCFA had conducted itself in a manner grossly inconsistent with the expectations clearly outlined in [the Office of Student Life’s] Student Organization Resource and Policy Guide, OSL has placed HCFA on a one year administrative probation,” Goldman wrote in the statement.

Goldman did not specify how HCFA, the largest Christian fellowship on campus, had violated Office of Student Life “expectations.” In an emailed statement Wednesday, HCFA co-presidents Scott Ely ’18 and Molly L. Richmond ’18 were slightly more specific.

From Trinitarian to Unitarian to Nadaitarian.

Item Who Are the Rich, White Men Institutionalizing Transgender Ideology?

I found exceedingly rich, white men with enormous cultural influence are funding the transgender lobby and various transgender organizations. These include but are not limited to Jennifer Pritzker (a male who identifies as transgender); George Soros; Martine Rothblatt (a male who identifies as transgender and transhumanist); Tim Gill (a gay man); Drummond Pike; Warren and Peter Buffett; Jon Stryker (a gay man); Mark Bonham (a gay man); and Ric Weiland (a deceased gay man whose philanthropy is still LGBT-oriented). Most of these billionaires fund the transgender lobby and organizations through their own organizations, including corporations.

Separating transgender issues from LGBT infrastructure is not an easy task. All the wealthiest donors have been funding LGB institutions before they became LGBT-oriented, and only in some instances are monies earmarked specifically for transgender issues. Some of these billionaires fund the LGBT through their myriad companies, multiplying their contributions many times over in ways that are also difficult to track.

These funders often go through anonymous funding organizations such as Tides Foundation, founded and operated by Pike. Large corporations, philanthropists, and organizations can send enormous sums of money to the Tides Foundation, specify the direction the funds are to go, and have the funds get to their destination anonymously. Tides Foundation creates a legal firewall and tax shelter for foundations and funds political campaigns, often using legally dubious tactics.

The tide is threatening to engulf us all.

March 23, 2018 | 2 Comments

(Hot) Air Let Out of California’s Global Warming Lawsuit

The California court climate class ordered by a judge has ended—with everybody agreeing the climate has changed.

This is good news. It means anybody who calls an oil company representative a “Climate denier!” from now on will either by lying or ignorant. This ought to make for quieter politics.

We saw earlier that the state of California was suing some oil companies.

Grant Me This

The concern was that because certain people were raking in a lot of money, they might have been tempted to skew research results in the direction of the money source. Well, it is a fallacy to say that because somebody took the government’s money to engage in climate research they necessarily shaded results in the government’s favor. But it does increase the chances.

What’s that? You think it’s only private concerns that are seduced by money? That, somehow, when an agency takes government funding all possible avenues of confirmation bias and enticements to please the hand that is feeding them are removed? How odd.

Anyway, California thought there might have been some kind of conspiracy by oil companies to hide secrets about global warming. We also saw that secrets of the kind hoped for by climate activists weren’t really possible.

But Judge William Alsup didn’t know that and so ordered both sides present to him a tutorial in the physics of externally heated fluids flowing over a rotating sphere. The class as scheduled to last a mere four hours. But, hey, what’s so difficult? Activists, politicians, celebrities, even reporters know all about this simple subject.

We’re Here to Help

Aiding his honor were two friend-of-the-court briefs, one of which was led by Viscount Monckton of Brenchley, Willie Soon, David Legates, Yours Truly, and others. The other was from scientists William Happer, Steven E. Koonin, and Richard S. Lindzen.

Happer and the others provided a lovely summary.

1. The climate is always changing; changes like those of the past half-century are common in the geologic record, driven by powerful natural phenomena

2. Human influences on the climate are a small (1%) perturbation to natural energy flows

3. It is not possible to tell how much of the modest recent warming can be ascribed to human influences

4. There have been no detrimental changes observed in the most salient climate variables and today’s projections of future changes are highly uncertain

Interested readers can explore the reasoning behind this four simple and true points at their leisure.

Monckton’s (my) group had two straightforward points.

First result:…there is no “consensus” among scientists that recent global warming was chiefly anthropogenic, still less that unmitigated anthropogenic warming has been or will be dangerous or catastrophic…

Second result:…even if it be assumed [for the sake of argument] that all of the 0.8 [degree Celsius] global warming since anthropogenic influence first became potentially significant in 1950 was attributable to us, in the present century little more than 1.2 [C] of global warming is to be expected, not the 3.3 [C] that the Intergovernmental Panel on Climate Change (IPCC) had predicted.

Again, you can…click here to read the rest.

Update Since writing, a new motion to enter another Amicus was submitted by the Concerned Household Electricity Consumers Council. Also, Chevron’s class notes are up.

March 14, 2018 | 16 Comments

Uncertainty: The Soul of Models, Probability & Statistics. Chapter Abstracts

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This post originally appeared right before the Uncertainty did. Now that we’re 1.5 years out, it’s time for a re-post. Buy it now, but it today, and buy it again tomorrow!

Chapter 1 :: Truth, Argument, Realism

Truth exists and we can know it. The universe (all there is) also exists and we can know it. Further, universals exist and we can know these, too. Any skepticism about truth, reality, or universals is self-refuting. There are two kinds of truth: ontological and epistemological, comprising existence and our understanding of existence. Tremendous disservice has been done by ignoring this distinction. There are two modes of truth: necessary and local or conditional. A necessary truth is proposition that is so based on a chain of reasoning from indubitable axioms or sense impressions. A local truth, and most truths are local, is so based on a set of premises assumed or believed true. From this seemingly trivial observation, everything flows, and is why so-called Gettier problems and the like aren’t problems after all. Science is incapable of answering questions about itself; the belief that it can is called scientism. Faith, belief, and knowledge are differentiated.

Chapter 2 :: Logic

Logical truth is conditional, as are all necessary and local truths, on the premises given or assumed. Logic is the study of the relation between propositions, between premises and conclusion, that is. So too is probability, which is the continuation, fullness, or completion of logic. All arguments use language, and therefore the terms, definitions, and grammar of language are part of the tacit premises in every argument. It is well to bring these tacit premises out when possible. Logic, like mathematics, is not empirical, though observations may inform logic and math, and logic and math may be used on empirical propositions. Probability, because it is part of logic, is also not empirical; and it, too, can be used on empirical propositions. Syllogistic is preferred over symbolic logic for its ease of understanding; syllogisms are an ideal way of grouping evidence. The fundamental principles of logic ultimately are not formal in a sense to be defined. Finally, not all fallacies are what they seem.

Chapter 3 :: Induction & Intellection

There is no knowledge more certain than that provided by induction. Without induction, no argument could, as they say, get off the ground floor. All arguments must trace eventually back to some foundation. This foundational knowledge is first present in the senses; through intellection, i.e. induction, first principles, universals, and essences are discovered. Induction is what accounts for our being certain, after observing only a finite number of instances or even one and sometimes even none, that all flames are hot, that all men are mortal, that for all natural numbers $x$ and $y$, if $x = y$, then $y = x$, and for providing content and characteristics of all other universals and axioms. Induction is analogical; it is of five different kinds, some more and some less reliable. That this multiplicity is generally unknown accounts for a great deal of the controversy over induction. Arguments are not valid because of their form but because of their content.

Chapter 4 :: What Probability Is

Probability is, like logic, an argument. Logic is the study of the relation between propositions, and so is probability. Like logic, probability is not a real or physical thing: it does not exist, it is not ontological. It cannot be measured with any apparatus, like mass or energy can. Like logic, probability is a measure of certainty of some proposition in relation to given or assumed premises—and only on these, and no other, premises, and this includes the tacit premises of language. All probability, without exception, is therefore conditional. Probability is widely misunderstood for two main reasons: the confusion between ontological and epistemological truth, and the conflation of acts or decisions with probability. We know the proposition “Mike is green” is true given “All dragons are green and Mike is a dragon”. This is an epistemological conditional, or local, truth. But we also know the major part of the premise is ontologically false because there are no dragons, green or otherwise. Counterfactuals are always ontologically false; i.e. they begin with premises known observationally to be false. Yet counterfactuals can have meaningful (epistemological) probabilities. Counterfactuals are surely meaningful epistemologically but never ontologically. Not all probabilities are quantifiable; most are not.

Chapter 5 :: What Probability Is Not

Logic is not an ontological property of things. You cannot, for instance, extract a syllogism from the existence of an object; the imagined syllogism is not somehow buried deep in the folds of the object waiting to be measured by some sophisticated apparatus. Logic is the relation between propositions, and these relations are not physical. A building can be twice as high as another building; the “twice” is the relation, but what exists physically are only the two buildings. Probability is also the relation between sets of propositions, so it too cannot be physical. Once propositions are set, the relation between them is also set and is a deducible consequence, i.e. the relation is not subjective, a matter of opinion. Mathematical equations are lifeless creatures; they do not “come alive” until they are interpreted, so that probability cannot be an equation. Probability is a matter of our understanding. Subjective probability is therefore a fallacy. The most common interpretation of probability, limited relative frequency, also confuses ontology with epistemology and therefore gives rise to many fallacies.

Chapter 6 :: Chance & Randomness

Randomness is not a thing, neither is chance. Both are measures of uncertainty and express ignorance of causes. Because randomness and chance are not ontologically real, they cannot cause anything to happen. Immaterial measures of information are never and can never be physically operative. It is always a mistake, and the cause of vast confusion, to say things like “due to chance”, “games of chance”, “caused by random (chance, spontaneous) mutations”, “these results are significant”, “these results are not explainable by chance”, “random effects”, “random variable”, and the like. All this holds in quantum mechanics, where the evidence for physical chance appears strongest. What also follows, although it is not at first apparent, is that simulations are not needed. This statement will appear striking and even obviously false, until it is understood that the so-called “randomness” driving simulations is anything but random. Coincidences are defined and their relation to cause explained. The ties between information theory and probability are given.

Chapter 7 :: Causality

Cause is analogical. There is not one type, flavor, or aspect of cause, but four. A formal, material, efficient, and final or teleological. Most causation concerns events which occur not separately, as in this before that, but simultaneously, where simultaneous events can be spread through time. Many causal data are embedded in time, and there two types of time series which are often confused: per se and accidental. These should not be mistaken for non-causal data series (the most common) which are all accidental. All causes are activiations of potentials by something actual. A vase is potential a pile of shards. It is made actually a pile of shards by an actual baseball. All four aspects of the cause are there: form of shards, clay fragments, efficient bat, and the pile itself as an end. Deterministic (and probability) models are epistemological; essential causal models are ontological and express true understanding of the nature of a thing. Causes, if they exist and are present, must always be operative, a proposition that has deep consequences for probability modeling. Falsifiability is rarely of interest, and almost never happens in practice. And under-determination, i.e. the possibility of causes other than those under consideration, will always be with us.

Chapter 8 :: Probability Models

A model is an argument. Models are collections of various premises which we assign to an observable proposition, i.e. an observable. Modelling reverses the probability equation: the proposition of interest or conclusion, i.e. the observable Y, is specified first after which premises X thought probative of the observable are sought or discovered. The ultimate goal is to discover just those premises X which cause or which determine Y. Absent these—and there may be many causes of Y—it is hoped to find X which give Y probabilities close to 0 or 1, given X in its various states. Measures of X’s importance are given. A model’s usefulness depends on what decisions are made with it, and how costly and rewarding those decisions are. Proper scores which help define usefulness are given. Probability models can and do have causative elements. Some probability models are even fully causal or deterministic in the sense given last chapter, but which are treated as probabilistic in practice. Tacit premises are added to the predictions from these models which adds uncertainty. Bayes is not all its cracked up to be. The origin and limitations of parameters and parametric models are given.

Chapter 9 :: Statistical & Physical Models

Statistical models are probability models and physical models are causal or deterministic or mixed causal-deterministic-probability models applied to observable propositions. It is observations which turn probability into statistics. Statistical and physical models are thus verifiable, and all use statistics in their verification. All models should be verified, but most aren’t. Classical modeling emphasizes hypothesis or “significance” testing and estimation. No hypothesis test, Bayesian or frequentist, should ever be used. Death to all p-values or Bayes factors! Hypothesis testing does not prove or show cause; therefore, embedded in every test used to claim cause is a fallacy. If cause is known, probability isn’t needed. Neither should parameter-centric (estimation, etc.) methods be used. Instead, use only probability, make probabilistic predictions of observables given observations and other premises, then verify these predictions. Measures of model goodness and observational relevance are given in a language which requires no sophisticated mathematical training to understand. Speak only in terms of observables and match models to measurement. Hypothesis-testing and parameter estimation are responsible for a pandemic of over-certainty in the sciences. Decisions are not probability, a fact with many consequences.

Chapter 10 :: Modelling Goals, Strategies, & Mistakes

Here are highlighted only a few of the most egregious and common mistakes made in modeling. Particular models are not emphasized so much as how model results should be communicated. The goal of probability models is to quantify uncertainty in an observable Y given assumptions or observations X. That and nothing more. This, and only this, form of model result should be presented. Regression is of paramount importance. The horrors to thought and clear reasoning committed in its name are legion. Scarcely any user of regression knows its limitations, mainly because of the fallacies of hypothesis testing and the over-certainty of parameter-based reporting. The Deadly Sin of Reification is detailed. The map is not the territory, though this fictional land is unfortunately where many choose to live. When the data do not match a theory, it is often the data that are suspected, not the theory. Models should never take the place of actual data, though they often do, particularly in time series. Risk is nearly always exaggerated. The fallacious belief that we can quantify the unquantifiable is responsible for scientism. “Smoothed” data is often given pride of place over actual observations. Over-certainty rampages across the land and leads to irreproducible results.

March 12, 2018 | 14 Comments

Courting Climate Comedy

The descent of climate science into the surreal is about to take another comedic slide. A judge has ordered the State of California and a handful of oil companies to present to him on 21 March “a two-part tutorial on the subject of global warming and climate change.”

Why? The cities of San Francisco and Oakland representing California are suing. They claim that British Petroleum and a group of other oil companies have created a “nuisance”. How? By not admitting, or hiding, or fibbing about what they knew about their product’s influence on global warming.

Of course, as we’ll see in a moment, nobody knows how much influence oil sales have on global warming. But the judge says he can learn all he needs to know about these kinds of attributions in court. Over four short hours.

The judge ordered that his first lesson should “trace the history of scientific study of climate change, beginning with scientific inquiry into the formation and melting of the ice ages, periods of historical cooling and warming, smog, ozone, nuclear winter, volcanoes, and global warming.”

Lawyers from California, or whatever experts they pay to be their honest mouthpieces, get one hour. Then oil companies, or their experts, get an hour.

Lesson two “will set forth the best science now available on global warming, glacier melt, sea rise, and coastal flooding.” Another hour for each side.

It’s anybody’s guess what the judge will make of the information he receives, or how it will play in the remainder of the trial. But given the state of disagreement among the world’s top experts, four hours isn’t enough to do justice to the subject.

Now if anybody tells you he can peg the exact contribution any company has made to global warming, he is either lying or a lunatic, or he is too in love with his slide rule.

The temptation to lie, perhaps by omission, when there is lots and lots and more than lots of money that can be taken from rich oil companies cannot be underestimated. Lunacy is unlikely, but can’t be dismissed, especially if any of the people involved in the trial are convinced the world is doomed unless we give up fossil fuels.

The slide-rule quip must be explained. In order to attribute a change in the climate to click here to read the rest.