Maggie's Farm

We have rreported so many scientific frauds in the past couple of weeks, I thought I would highlight some commonly-used "data-management" tricks designed to dishonestly influence people.

1. "Clustering." We have all heard about cancer clusters - Why does my town have triple the breast cancer of towns two miles away? There must be someone I can sue about this. Such claims have an emotional appeal, but they are nonsense. Random distribution is not even - it is uneven. Just try flipping a quarter, and you will get little runs of tails. Clustering is a natural effect of randomness, but trial lawyers are always busy trying to track them down: they can get rich before anyone figures out the game.

2. "Cherry-picking."  Cherry-picking is a frankly dishonest form of data presentation, often used by newspapers to create alarmist stories about the economy, the environment, food safety, etc. It fools people without some decent science education. What it entails is combing through, say, 60 pieces of data, and then using the three points that support your argument, and ignoring the rest. Presenting random changes as meaningful facts is a lie. Environmentalists use this all of the time, as do other agenda-driven fact-handlers. A casual use of this fallacy is characteristic of The New York Times typical headline: Despite Good Economic Statistics, Some Are Left Behind - and then they scour NYC to find some single black mom in the Bronx who cannot support her kids - and she becomes the "story".

3. "Anectdotal evidence." The above example could also be termed "anectdotal evidence." If you look around, you can always find an exception, a story, and example - of ANYTHING. But anectdotes are compelling, and Reagan used them to the best effect. And how about those swimming Polar Bears! (I always thought they liked to swim.)

4. "Omitted evidence".  You tell me how common this is! A first cousin of Cherry-picking, Omitted Evidence is also a lie. All you do is ignore the evidence and data that disagrees with your bias or your position. Simple.

5. "Confirmation bias".  People tend to remember evidence which supports their opinion, belief, or bias, and to dismiss or forget evidence which does not. It's a human frailty. Humans have to struggle to be rational.

6. "Biased Data".  "A poll at a local pre-school playground in Boston at 2 pm today indicated that 87% of likely voters will vote for Obama." Picking your data sources, like picking the questions you ask, can determine your results with great accuracy. As pollsters always say, "Tell me the answer you want, and I will design the question."

7. "Data mining." Data-mining is used by unscrupulous academics who need to publish. Because it is a retroactive search for non-hypothesized correlations, it does not meet criteria for the scientific method. Let's say you have 10,000 data points from a study which found no correlation for your hypothesis. Negative correlation studies are rarely published, but you spend a lot of time collecting it - so you ask your computer if it can find any other positive correlations in the data. Then you publish those, as if that was what you had studied in the first place.

Image: two good varieties of cherries for picking; Stella on the left, Lapins on the right, from Miller Nurseries

Whether the "research" is about medicines, climate, sociology, or whatever, greedy human nature always finds a place for itself.  This is why we, at Maggie's, always assume a skeptical posture towards "studies."

Most "studies" show that researchers want to get money and jobs for doing studies.

In the past year, we have been overwhelmed with the sleaziness of the warmist crowd and the social psychology crowd, but today we find pay to play in university education research.

Almost everybody has an agenda, even scientists. It's human. That's why we remain skeptics about everything. Call it cynical if you want, but we think it's being realistic.

Recent scandals in psychology demonstrate how easy it is to massage data, or even twist and invent data, in order to produce a desired result. In this report, some psychologists show how it is done:

What the researchers omitted, as they went on to explain in the rest of the paper, was just how many variables they poked and prodded before sheer chance threw up a headline-making result—a clearly false headline-making result.

The odds of statistical bogosity grow when researchers don't have to report all the ways they manipulated their data in exploratory fashion. For example, the researchers "used father's age to control for baseline age across participants," thereby fudging the subjects' actual ages. They factored in lots of completely irrelevant data. And, rather than establish from the outset how many subjects they would test, they tested until they obtained the false result.

The authors of that provocative paper were Joseph P. Simmons and Uri Simonsohn of the University of Pennsylvania, and Leif D. Nelson of the University of California at Berkeley. "Many of us," they wrote—"and this includes the three authors of this article"—end up "yielding to the pressure to do whatever is justifiable to compile a set of studies that we can publish. This is driven not by a willingness to deceive but by the self-serving interpretation of ambiguity. ... "

An RCT is a "randomized controlled clinical trial."

We have discussed the scientific fallacy of "data mining" here in the past in which, instead of testing an hypothesis (aka the Scientific Method), the researcher simply asks the computer to find any correlations in the mountain of collected data. That is not science. This is typically done when a researcher has a mound of data which did not support his hypothesis. So as not to waste it, he asks the computer to find something else in it.

In any mountain of data, some correlations can be found if only by laws of randomness - see the legal hoax of so-called Cancer Clusters.

Often enough, when you read "Study says...", you are reading a report from data mining. Our readers know that a statistical correlation often - or usually - means nothing, but data-mining "information" is non-information. Generally speaking, newspaper reporters never passed Statistics 101. (I did, but found stats difficult to explain to innumerate juries who even get confused by basic algebra.)

Junkfood Science discusses Beware the RCT. One quote:

Even medical professionals get taken by this growing technique. It's most common when secondary studies use the database from participants in a randomized controlled trial to look for correlations not to scientifically test a hypothesis, let alone one the original trial had been designed to fairly test. Carefully controlled clinical trials are concerned with causes and effective treatments. In contrast, multivariate analyses of large databases, with their statistical manipulations and regression computer modeling, are statistics. Statistics is about correlations. It's not biological research.

Dr. Merc does not seem to believe that we're all going to drown anytime soon.

However, the science is settled (via Watts): there is probably or possibly a short-term (centuries) warming trend, if the data is worth anything (about which I am a skeptic). Nothing to think twice about unless you plan on bringing farming back to Greenland in 300 years:

Note the dramatic correlation with global CO2 emissions! None. Here's a better correlation which shows some real proof: Global temperatures caused by decrease of Mediterranean pirates.

QED - it's a linear inverse relationship This cause is therefore settled science, and the obvious solution to refrigerate ourselves is to import more pirates into the Med until we are cold enough.

Big surprise: Federal flood insurance encourages people to live in flood zones!

Who could have anticipated that? Taxpayers bribe people to live in flood zones. Brilliant! For total stupidity, NOLA is not even a flood zone - it is permanently below sea level, and always has been. Why am I, who made the reasonable decision to live above sea level, responsible for the life choices of people who want to live underwater?

And, of course, flood zones and flood plains are basically "wetlands." One might think these places should be protected from development for environmental and flood control reasons. A farm? OK, if you understand that it will periodically get flooded while being delivered a good supply of fresh, healthy silt for your next crop.

I know about flood zones. Part of our property is in one. We keep it in horse pasture, and our pool is down there. House and barns are above. People in 1786 weren't stupid, and they did not expect the government, ie their neighbors, to protect them from nature.

Furthermore, if you believe Al Gore that the water is rising, perhaps we should be bribing people to move further from water...but nobody believes Al Gore anymore.

A perfect example of it: Climate Confusion: Global Warming Halted by Pollution.

The alarmists are playing whack-a-mole with any data which does not fit their hypotheses and predictions. This is the stuff of politicians, children, and litigators, not scientists.

One definition: An error in reasoning in which one assumes that the observed relationship between current events and some historical events represents a causal relationship.

Such reasoning is not consistent with the scientific method. When data don't fit your hypothesis, you can't makes excuses for your data while leaving your hypothesis unchanged.

If you play that game, you also violate the rules of Falsifiability by making a non-falsifiable hypothesis. My bold:

(Karl Popper) A conjecture or hypothesis must be accepted as true until such time as it is proven to be false. Popper maintains that scientists approach the truth through what he calls "conjecture and refutation." In actuality, scientists approach the truth not through conjecture and refutation, but through conjecture and CONFIRMATION, i.e. demonstration, by means of careful experiment, that a hypothesis corresponds to the facts of reality. Until the phenomenon is proven TRUE there is no obligation to base my attitude toward it on the assumption that it MIGHT be true. If there were such an obligation, then I would be obliged to give serious consideration to every crackpot notion that has ever been put forward.

If an hypothesis cannot be refuted by data, it's not science: it's a belief system. The evidence that there has been no warming for over a decade is difficult data indeed in light of their hysterical predictions, so now they have invented covert warming. This is pathetic and embarassing. Tweaking computer models to fit unexpected data is not science. It's overt fudging.

As a commenter at Watts pointed out, with some math adjusting you can prove Ptolemy's solar system to be an accurate model.

(Thanks to Hogeye Bill's Dictionary of Logical Fallacies)

Related: Breaking: A peer reviewed admission that “global surface temperatures did not rise between 1998 and 2008″ – Dr David Whitehouse on the PNAS paper Kaufmann et al. (2011). The comments there are great. One example:

What... emerges from this is a convenient flexible device to explain any climate change and blame it on humans. Is there warming? Its caused by CO2. Cooling? It can only be smokestack particles.

Honest discussions or debates have one purpose: to illuminate a subject with facts and theories which relate facts to eachother, and perhaps to persuade. Dishonest debates or arguments are really just fights with words, and of interest only to litigators and politicians.

Fallacious arguments  of the false assumption type are used in both: in the former by accident or out of ignorance, and in the latter as a tactical trick (eg "If the glove don't fit, you must acquit.").

When questions are posed in that manner, they are known as loaded questions.  They are "loaded" with an effort to seek your acquiescence to an unspoken assumption. (The classic is "When did you stop beating your wife?") The correct response to questions with hidden assumptions is to point that out, and to challenge the hidden assumption. Otherwise, you will fall into a trap.

When engaging on an issue, always examine the other guy's assumptions first, because a topic can go nowhere with fallacious assumptions, and there can be no constructive discussion if you do not accept the other guy's premise. In that case, you must address the premise first, backing up before you can move forward.

Here are some simple examples of fallacious assumptions. Usually, in arguments, the assumptions are unstated, "assumed." It's better to state them first just as one lists one's "givens" in geometry proofs. Sometimes, just addressing the assumptions clears everything up.

 John at Powerline: You Can Prove Anything If You Make the Right Assumptions. Certainly true, if one is engaging in dishonest or tendentious debate.

(I am aware that I am not discussing the huge and important topic of unconscious assumptions, but that is more about psychology than logical debate.)

That is, of course, the title of Jones' classic, a companion piece to Huff's How To Lie with Statistics.

A post at Watts explains how the graph on right is designed to be misleading, to say the least.

I have been neglecting my Fallacy portfolio here at Maggie's for quite a while. My bad.

Category Error is not a complex notion, but it was formulated in a somewhat complex way by the brilliant Gilbert Ryle in his classic work, The Concept of Mind (this via Wiki):

The term "category-mistake" was introduced by Gilbert Ryle in his book The Concept of Mind (1949) to remove what he argued to be a confusion over the nature of mind born from Cartesian metaphysics. Ryle alleged that it was a mistake to treat the mind as an object made of an immaterial substance because predications of substance are not meaningful for a collection of dispositions and capacities.

Specifically, the phrase is introduced in chapter 1, section 2. The first example he gives is of a visitor to Oxford. The visitor, upon viewing the colleges and library, reportedly inquired “But where is the University?"^[1] The visitor's mistake is presuming that a University is part of the category "units of physical infrastructure" or some such thing, rather than the category "institutions", say, which are far more abstract and complex conglomerations of buildings, people, procedures, and so on.

Ryle's second example is of a child witnessing the march-past of a division. After having had battalions, batteries, squadrons, etc. pointed out, the child asks when is the division going to appear. 'The march-past was not a parade of battalions, batteries, squadrons and a division; it was a parade of the battalions, batteries and squadrons of a division.' (Ryle's italics)

His third example is of a foreigner being shown a cricket match. After being pointed out batsmen, bowlers and fielders, the foreigner asks: 'who is left to contribute the famous element of team-spirit?'

He goes on to argue that the Cartesian dualism of mind and body rests on a category-mistake.

Yes, I think it does. But... I think, therefore I post things at Maggie's Farm. From another site, here's a simple formulation of this common and basic fallacy:

These fallacies occur because the author mistakenly assumes that the whole is nothing more than the sum of its parts. However, things joined together may have different properties as a whole than any of them do separately. The following fallacies are category errors:

• Composition (Because the parts have a property, the whole is said to have that property)
  
• Division (Because the whole has a property, the parts are said to have that property)

Give us some solid examples. I don't have time think up some good ones today. Duty before pleasure.

Stick with it, and get past the Polar Bear example to the Einstein: A Hipbone Approach IV: Polar bears and polar opposites. He quotes CS Lewis:

What flows into you from the myth is not truth but reality (truth is always about something but reality is about which truth is) and therefore every myth becomes the father of innumerable truths…

I could write, and run in circles, on this topic for days. So I won't. Plus I promised She Who Must Be Obeyed that I would clean up the kitchen. Fact.

Via Legal Insurrection:

True � The statement is accurate and there�s nothing significant missing.
Mostly True � The statement is accurate but needs clarification or additional information.
Half True � The statement is accurate but leaves out important details or takes things out of context.
Barely True � The statement contains some element of truth but ignores critical facts that would give a different impression.
False � The statement is not accurate.
Pants on Fire � The statement is not accurate and makes a ridiculous claim.

In the political world, there are all sorts of truth. In the real world, only one sort. In the real world, a half-truth is a lie.

I guess we don't have to worry about that baloney. I was hoping a good one would hit up here, so I could have an excuse for a day or two off work to catch up on my chores. We know how to deal with bad weather.

Globalistical warmening fails again. Funny how negative results never make headlines or get attention. Even important negative findings in science have trouble getting published in scientific journals. There must be a fallacy term for that, but I'm not sure what it is. If a good hurricane hit land this year, the Al Gore folks would be all over it.

What am I smoking? A Griffin corona. Nice. No, actually, it's not a corona. It's bigger than that. Tasty, whatever the Griffin is that I bought from my local upscale cigar store Indian.

A useful term - a noun - for "a problem that severely tests the ability of an inexperienced person." More generally, a problem or challenge which will separate the bright and the perceptive from the not-so-bright and the not-so-perceptive.

"Bridge of asses." Donkeys do not like to cross bridges.

In mathematics, the term is applied to the problem from the first book of Euclid that if two sides of a triangle are equal then the angles opposite those sides are also equal.

Traditionally, the bridge of asses referred to Euclid's Fifth Theorem of planar geometry, the comprehension of which and the implications of which were and are a sticking point for less-bright students.

By the way, this is a good if somewhat challenging book: Experiencing Geometry. A bit of a pons asinorum itself.

That's a term which has come into fashion lately, and it deserves to. Counterfactuals are a specific variety of BS, as our commenter notes.

The term can be used as a noun or as an adjective. The key to it is its conditionality (If...,then...might have...); the past subjunctive, combined with its lack of factual content. For example, "You bozo - you left a burner on. You could have burned down the house." Well maybe - but it did not happen. Thus no fact.

(An "indicative conditional," by way of contrast, is a past conditional which is founded on a real, factual consequence which occurred. For example, "If you bozos hadn't left the gate open, the dog would not have run into the street." Indicative conditionals are also debatable, due to their speculative nature, ie, cum hoc ergo propter hoc. For example, in my case, almost every time I water the garden, it rains afterewards.)

Counterfactuals are often used (abused) to make emotional arguments. "If the stimulus had been 3 trillion dollars, our unemployment rate would be 4%."

Free Dictionary offers this:

adj

(Philosophy / Logic) expressing what has not happened but could, would, or might under differing conditions

n

(Philosophy / Logic) a conditional statement in which the first clause is a past tense subjunctive statement expressing something contrary to fact, as in if she had hurried she would have caught the bus.

It's about the difficulty in knowing what you don't know, and the limits of self-observation. From this site (h/t, Coyote's Arrogant Ignorance):

…people who lack the knowledge or wisdom to perform well are often unaware of this fact. We attribute this lack of awareness to a deficit in metacognitive skill. That is, the same incompetence that leads them to make wrong choices also deprives them of the savvy necessary to recognize competence, be it their own or anyone else’s.

What's a "metacognitive skill"? It's about "the ability to reflect and assess ones' own thinking and understanding."

If I did not suffer from a mild case of Dunning-Kruger, I would not be able to post anything on Maggie's Farm because "I don't know anything, I never did know anything, but now I know that I don't know...":

"Cooperation" has been the mantra of the Kindergarten-minded in our midst for years. "Competition" is supposedly male, leads to Capitalism and war and other not-nice things, and is thus evil and a human trait which must be eliminated.

Of course, I have never noticed women to be any less competitive than men. Everybody enjoys a bit of the spice of competition in life, even when you lose.

Competition vs Cooperation a phony duality which, I assume, comes from some wacky ideology. Case in point: The Dark side of Cooperation.

Robin Hanson begins his piece of the above title, about "forager norms," thus:

Food isn’t about Nutrition
Clothes aren’t about Comfort
Bedrooms aren’t about Sleep
Marriage isn’t about Romance
Talk isn’t about Info
Laughter isn’t about Jokes
Charity isn’t about Helping
Church isn’t about God
Art isn’t about Insight
Medicine isn’t about Health
Consulting isn’t about Advice
School isn’t about Learning
Research isn’t about Progress
Politics isn’t about Policy

His conclusion: "We signal covertly and unconsciously because our ancestors were strongly punished for overt and conscious signals."

Signaling theory is interesting, but I do not accept the reductionistic notion that signaling is all that people do when they are together (I should say, neither does Robin H.).

At Chicago Boyz, Seizing the Opportunity to Destroy Western Civilization. A quote:

Narrative reduces personal experience to a linear progression where cause and effect seem to have a purposeful order. These narratives can then be shared with others, leading to the best definition of history ever: history is a fable agreed upon. Most personal narratives will never be the equivalent of the histories of Herodotus, Thucydides, or Gibbon; or even the fables of Aesop, La Fontaine, or Orwell. Most are simply humble habitual ways of thinking. As habits of thought, such narratives largely control how you react to unfolding events, whether it’s pricking your finger or waging a world war. 

Prof. Lindzen, in his talk at Fermilab which we posted yesterday, refers to the Prosecutor's Fallacy (aka Defender's Fallacy), which refers to a strategy of counting on a jury's inability to understand statistics, and specifically conditional probability.

Conditional probability is about the amount of linkage in events.

The simpest case: Given a red, green and blue marble in a bag, what are the odds of drawing a blue one after drawing a red one?

See the sad case of Sally Clark, who fell victim to the fallacy.

Ioannidis' Why Most Published Research Findings Are False. In his summary, he observes:

Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

I had read his paper before, but it seems especially relevant now. h/t, Classical Values.

Name that fallacy.

I would expect better from those guys.

"We have a test for a rare disease (we’ll call it Jones Syndrome), and the test is 99% accurate, but it returns a false positive in 1% of those tested (that is, 1% of the time the test returns a positive, the disease is not present). If I test positive, what is the probability that I have Jones Syndrome?"

It's not a trick question, it's a question of simple logic - and that's why it's so easy to fool juries with this sort of thing.

OK, we'll add this data:

"How prevalent is Jones Syndrome, that is, what is the probability of my having it, irrespective of any test result? We’ll say that 1 in 10000 have Jones Syndrome, so your untested probability of having Jones Syndrome is 0.01%, or 0.0001."

Answer is below the fold.  Explanation at Right Wing Prof

Via Icecap:

Leading UK Climate Scientists Must Explain or Resign

By Jenniffer Marohasy

MOST scientific sceptics have been dismissive of the various reconstructions of temperature which suggest 1998 is the warmest year of the past millennium. Our case has been significantly bolstered over the last week with statistician Steve McIntyre finally getting access to data used by Keith Briffa, Tim Osborn and Phil Jones to support the idea that there has been an unprecedented upswing in temperatures over the last hundred years - the infamous hockey stick graph. 

Mr McIntyre’s analysis of the data - which he had been asking for since 2003 - suggests that scientists at the Climate Research Unit of the United Kingdom’s Bureau of Meteorology have been using only a small subset of the available data to make their claims that recent years have been the hottest of the last millennium.  When the entire data set is used, Mr McIntyre claims that the hockey stick shape disappears completely. (Yamal: A “Divergence” Problem, by Steve McIntyre, 27 September 2009)

Mr McIntyre has previously showed problems with the mathematics behind the ‘hockey stick’.  But scientists at the Climate Research Centre, in particular Dr Briffa, have continuously republished claiming the upswing in temperatures over the last 100 years is real and not an artifact of the methodology used - as claimed by Mr McIntyre. However, these same scientists have denied Mr McIntyre access to all the data.  Recently they were forced to make more data available to Mr McIntyre after they published in the Philosophical Transactions of the Royal Society - a journal which unlike Nature and Science has strict policies on data archiving which it enforces. 

This week’s claims by Steve McInyre that scientists associated with the UK Meteorology Bureau have been less than diligent are serious and suggest some of the most defended building blocks of the case for anthropogenic global warming are based on the indefensible when the methodology is laid bare. 

This sorry saga also raises issues associated with how data is archived at the UK Meteorological Bureau with in complete data sets that spuriously support the case for global warming being promoted while complete data sets are kept hidden from the public - including from scientific sceptics like Steve McIntyre.

It is indeed time leading scientists at the Climate Research Centre associated with the UK Meteorological Bureau explain how Mr McIntyre is in error or resign. See post here.

Looks like they were cherry-picking data to get the results they wanted. Why?

Collect your best debating points here: Hawk vs. Dove on crime and punishment. Dalrymple.

As a retired prison shrink, Dalrymple knows whereof he opines. 

More fun with basic math today. Sam Savage on why we underestimate risk (h/t, Theo):

I do enjoy it when others do my work for me.

This piece, Putting Obama on the Couch (h/t, Cafe Hayek) offers a few of my favorite cognitive biases: Wishful Thinking, Planning Fallacy, Overconfidence Effect, Attentional Bias and Anchoring Bias.

A repost from 2007:

Today during lunch I read the piece by Tyler at Tangled Web about Dawkins, who is hopeful that a "final scientific enlightenment" will destroy religion on earth. Dawkins thinks it might require that elusive "theory of everything" to do the job.

Tyler correctly notes that the "theory of everything" will never address mankind's eternal questions.

Then I followed a link in one of his commenters to an essay by physicist Stanley Jaki, who makes the case that the "Theory of Everything" must be subject to Godel's Theorem. Very interesting essay, but I cannot cut and paste from it. Read it. He discusses Stephen Hawkings' epiphany, after many years of championing the quest, that a "theory of everything" is impossible.

Then I went over to Wikipedia to refresh my vague recollections of Godel's Incompleteness Theorem, which has nothing in common with Heisenberg's Uncertainty Principle. That Wikipedia entry was good, but there was some rough sledding in it.

And that led me to the entry on The Liar Paradox.

The Liar Paradox is the old "Nothing I say to you is true,"  and the many variations thereof.

Is the Liar Paradox a true paradox, or an artifact of symbolization? I think the latter, but that reveals my bias of expecting consistency from reality. If you're curious about the approaches to the puzzle, the Wikipedia entry seems to do a good job with it.

Thus passed a very enjoyable Tuesday lunch break for this dilettante. (The Escher image is perfect, Bird Dog - thanks.)

Update: Here's a piece that takes you deeper into the Liar Paradox. Thanks, BL

Cognitive biases aren't formal logical fallacies, but I put them in my Fallacy Collection anyway.

In one short post in the realm of economics titled When Stupid is Smart, Stumbling hits on a bunch of my old favorites: The Gambler's Fallacy, The Hot Hand Fallacy, the Focusing Effect, The Status Quo Bias, Wishful Thinking, and the like.

He makes the point, as we have done here in the past, that cognitive biases save time, and that sometimes a quick, suboptimal decision is better than a slow, perfect one. Sometimes.

It's a twofer from Humbug: Name That Fallacy! 

I have always been a fan of False Dilemma. It works like a dream on the naive. Perfect Solution is for children and utopians.

"Ever done the opposite of what the experts say?"

Our friend Stumbling hits a handful of fallacies in a piece on experts. One quote:

...herein lies the purpose of experts. It’s to reinforce these mechanisms, to help people avoid the uncomfortable facts that the world is uncertain, that mistakes are inevitable, and that we are not as in control of things as we think.

Blaming experts for being wrong is like complaining that the economy is not yellow. It’s a category error so howling as to be nonsensical.

Readers know that I am a collector of formal fallacies. I keep them on the mantle, well-dusted and polished. "Saving the Hypothesis" doesn't strike me as a formal, Aristotelian fallacy, but it surely is a common thing for folks to struggle to salvage a notion in which they are emotionally invested, regardless of new data. We all do that sometimes unless we catch ourselves BSing ourselves. 

Larry Anderson at American Thinker proposed this fallacy in relation to the Global Warming Climate Change whatever topic. One quote:

I distinctly remember the first time I had an argument with someone about "climate change."  It was a warm spring day in April of 1975. I was walking across the campus at Harvard headed for lunch. A fellow classmate (we were both juniors) ran up to me. He was really excited. He hollered out:

"Have you heard? The ice age is coming. They've proven it with computer studies at MIT."

"How did they prove that there is an ice age coming with computers?" I asked my friend.

"The planet is getting colder. They have the data.  And it is going to keep getting colder. They have these computer models --"

I stopped him right there. I knew enough about computers to understand that they were not up to accurately predicting short-term weather patterns -- let alone an ice age.

"No way. Computers aren't that powerful. "

"But they have the data and they have computers!"

"I know they do. But computers spit out whatever they are programmed to spit out. Load a computer with the data that the world has been getting colder; ask it what the weather will be tomorrow, and what do you think the computer is going to tell you? The world is getting hotter? If it does you'd better get a better computer. You hungry?" I replied and I continued on my way to lunch.

The original "climate change" hypothesis was that the planet was getting colder and that it would continue getting colder. That was a very simple hypothesis and was easily proven or refuted. Planet keeps getting colder = hypothesis correct. Planet gets warmer = hypothesis incorrect.

The world got warmer instead of colder. The "climate change" hypothesis was rewritten. This time the planet was facing a catastrophic meltdown. The world was not only getting warmer -- it was going to keep getting warmer at an ever increasing and life-threatening rate.

Think you're smart? That could mean that you are not. Always listen to different view before rejecting them. From a piece with the above title at Overcoming Bias:

...it seems clear that mostly what is going on is that we all misjudge our ability and task difficulty. 

You can say that again.

The article in Zombietime, The Left's Big Blunder, is about the famous Asch experiments on conformity.

Obama supporters presume that increasing Obama's perceived support will induce informational conformity in the American public. In other words, Obama supporters operate on the assumption that individual McCain supporters or undecided voters will in actuality change their minds about who to vote for if they perceive that a majority of people are supporting Obama. The imagined line of thinking is, "Gee, if so many people like this Obama guy, then my impression of him must be wrong; I trust the group's wisdom more than my own impressions."

I suppose the Dems' efforts to portray Obama as inevitable and wildly popular are an effort to exploit the conformist tendencies in people, but this seems rather usual in politics. However, the article offers a good depiction of how conformity can distort our thinking and cause us to doubt ourselves and our lyin' eyes. 

The desire to fit in and to be accepted is strong in all of us. It's important for survival, but we need to try to make room for thinking for ourselves too, based on our experience and not on received opinion.

Just as professional journals and newspapers are more interested in printing pieces that support hypotheses rather than papers with negative findings, so we all tend to spend more time discussing the risks of action rather than the risks of inaction.

That seems to be human nature, but it ain't rational and, fortunately, people vary across a spectrum of activity/passivity.

Passive people worry about the risks of action. Active people worry about the risks of inaction. I am more-or-less in the middle.

To discuss that half-intelligently, though, I first need to review the notion of Type 1 and Type 2 errors, now that we have taken a look at the null hypothesis a couple of days ago. 

A Type 1 error, also known as False Positive, is the error of erroneously rejecting the null hypothesis. In other words, it supports a connection which does not really exist.

A Type 2 error, or False Negative, is the error of wrongly accepting the null hypothesis. In other words, it says nothing is there, when it is, in fact, there.

For example, a blood test which has a 10% False Positive rate will wrongly tell you that there is an abnormality 10% of the time. A blood test with a 10% False Negative rate will miss an abnormality 10% of the time.

For another example, convicting an innocent person is a Type 1 error; letting a guilty person go free is a Type 2.

Depending on the matter at hand, either sort of error could have worse consequences. A Type 1 error in a death penalty case is a grievous error. But sometimes you need Type 1 errors.

My favorite example of a good Type 1 error is in the emergency treatment of appendicitis. Since medical diagnosis contains both art and luck as well as science, some error rate is inevitable unless you have the diseased organ in hand. But since a False Negative diagnosis would have dire consequences (ruptured appendix), it is necessary to do some unnecessary appendectomies on patients who might have appendicitis, but do not turn out to. In the case of emergency appendectomies: one study indicates that the Type 1 error rate is around 10%, with 18% False negatives. I would have guessed that the False Positives would be higher, and you could argue that there is room for them to go higher. The point is that, with appendicitis, you want to minimize your False Negatives by having more False Positive diagnoses - by being deliberately biased against the Null Hypothesis that there is nothing there, but without cutting open everyone with a bad stomach ache.

Thus that is the opposite of what you want in a justice system, where the null hypothesis of innocence is presumed in order to minimize False Positives.

From Wiki:

The conjunction fallacy is a logical fallacy that occurs when it is assumed that specific conditions are more probable than a single general one.

The most oft-cited example of this fallacy originated with Amos Tversky and Daniel Kahneman ^[1]:

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Which is more probable?

1. Linda is a bank teller.
2. Linda is a bank teller and is active in the feminist movement.

85% of those asked chose option 2 ^[2]. However, mathematically, the probability of two events occurring together (in "conjunction") will always be less than or equal to the probability of either one occurring alone.

Thus is the brain seduced by detail. All writers know this fact. Which works best: "Dick wore a hat." or "Dick wore a green felt hat with a pheasant-feather hatband."

One of my favorite sites to visit, Overcoming Bias, wonderfully discusses When not to use probabilities. He says:

The laws of probability are laws, not suggestions, but often the true Law is too difficult for us humans to compute.  If P != NP and the universe has no source of exponential computing power, then there are evidential updates too difficult for even a superintelligence to compute - even though the probabilities would be quite well-defined, if we could afford to calculate them.

So sometimes you don't apply probability theory.  Especially if you're human, and your brain has evolved with all sorts of useful algorithms for uncertain reasoning, that don't involve verbal probability assignments.

Those algorithms are "gut feelings." Often wrong, often accurate. There is one thing that I know for certain: the more time I have to think about how to hit a tennis ball, the more likely I am to blow the shot. He also says:

In general a rationalist tries to make their minds function at the best achievable power output; sometimes this involves talking about verbal probabilities, and sometimes it does not, but always the laws of probability theory govern.

If all you have is a gut feeling of uncertainty, then you should probably stick with those algorithms that make use of gut feelings of uncertainty, because your built-in algorithms may do better than your clumsy attempts to put things into words.