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Creative Commons has in the past been struggling with the fact that the majority of users tends to adopt more restrictive licenses. The organization estimated that two out of three Creative Commons-licensed works can’t be reused commercially, and one out of four can’t be reincorporated into a new work at all.

CC-BY on the other hand allows commercial reuse as well. This doesn’t just open YouTube and its producers new revenue opportunities it also makes it possible to reuse these videos in a much wider variety of contexts. Wikipedia, for example, demands that any videos posted to its site can be reused commercially. Combine that with the fact that YouTube has been converting its entire catalog into the open source WebM format, and there’s little reason why tens of thousands of Creative Commons-licensed YouTube videos shouldn’t show up on Wikipedia any day now.

it still leaves a lot of areas open for linguists to argue about. And the study did not build an exhaustive tree of any of the language families, in part because we probably don't have enough information to classify all of them at this point.

Still, it's hard to imagine any further details could overturn the gist of things, given how badly features failed to correlate across language families. And the work might be well received in some communities, since it provides an invitation to ask a fascinating question: given that there aren't obvious word order patterns across languages, how does the human brain do so well at learning the rules that are a peculiarity to any one of them?

One of the technologies where extremism is playing out these days is in Google’s own backyard. While citizen empowerment movements have made use of YouTube to broadcast their messages, so have Terrorist and other groups. Just this week, anti-Hamas extremists kidnapped an Italian peace activist and posted their hostage video to YouTube first before eventually murdering him. YouTube has been criticized in the past for not removing violent videos quick enough. But Cohen says the conference is looking at the root causes that prompt a young person to join one of the groups in the first place. "There are a lot of different dimensions to this challenge," he says. "It’s important not to conflate everything."

Experts in the math of probability and statistics are well aware of these problems and have for decades expressed concern about them in major journals. Over the years, hundreds of published papers have warned that science’s love affair with statistics has spawned countless illegitimate findings. In fact, if you believe what you read in the scientific literature, you shouldn’t believe what you read in the scientific literature.

“There are more false claims made in the medical literature than anybody appreciates,” he says. “There’s no question about that.”Nobody contends that all of science is wrong, or that it hasn’t compiled an impressive array of truths about the natural world. Still, any single scientific study alone is quite likely to be incorrect, thanks largely to the fact that the standard statistical system for drawing conclusions is, in essence, illogical. “A lot of scientists don’t understand statistics,” says Goodman. “And they don’t understand statistics because the statistics don’t make sense.”

In 2007, for instance, researchers combing the medical literature found numerous studies linking a total of 85 genetic variants in 70 different genes to acute coronary syndrome, a cluster of heart problems. When the researchers compared genetic tests of 811 patients that had the syndrome with a group of 650 (matched for sex and age) that didn’t, only one of the suspect gene variants turned up substantially more often in those with the syndrome — a number to be expected by chance.“Our null results provide no support for the hypothesis that any of the 85 genetic variants tested is a susceptibility factor” for the syndrome, the researchers reported in the Journal of the American Medical Association.How could so many studies be wrong? Because their conclusions relied on “statistical significance,” a concept at the heart of the mathematical analysis of modern scientific experiments.

Statistical significance is a phrase that every science graduate student learns, but few comprehend. While its origins stretch back at least to the 19th century, the modern notion was pioneered by the mathematician Ronald A. Fisher in the 1920s. His original interest was agriculture. He sought a test of whether variation in crop yields was due to some specific intervention (say, fertilizer) or merely reflected random factors beyond experimental control.Fisher first assumed that fertilizer caused no difference — the “no effect” or “null” hypothesis. He then calculated a number called the P value, the probability that an observed yield in a fertilized field would occur if fertilizer had no real effect. If P is less than .05 — meaning the chance of a fluke is less than 5 percent — the result should be declared “statistically significant,” Fisher arbitrarily declared, and the no effect hypothesis should be rejected, supposedly confirming that fertilizer works.Fisher’s P value eventually became the ultimate arbiter of credibility for science results of all sorts

But in fact, there’s no logical basis for using a P value from a single study to draw any conclusion. If the chance of a fluke is less than 5 percent, two possible conclusions remain: There is a real effect, or the result is an improbable fluke. Fisher’s method offers no way to know which is which. On the other hand, if a study finds no statistically significant effect, that doesn’t prove anything, either. Perhaps the effect doesn’t exist, or maybe the statistical test wasn’t powerful enough to detect a small but real effect.

Soon after Fisher established his system of statistical significance, it was attacked by other mathematicians, notably Egon Pearson and Jerzy Neyman. Rather than testing a null hypothesis, they argued, it made more sense to test competing hypotheses against one another. That approach also produces a P value, which is used to gauge the likelihood of a “false positive” — concluding an effect is real when it actually isn’t. What  eventually emerged was a hybrid mix of the mutually inconsistent Fisher and Neyman-Pearson approaches, which has rendered interpretations of standard statistics muddled at best and simply erroneous at worst. As a result, most scientists are confused about the meaning of a P value or how to interpret it. “It’s almost never, ever, ever stated correctly, what it means,” says Goodman.

experimental data yielding a P value of .05 means that there is only a 5 percent chance of obtaining the observed (or more extreme) result if no real effect exists (that is, if the no-difference hypothesis is correct). But many explanations mangle the subtleties in that definition. A recent popular book on issues involving science, for example, states a commonly held misperception about the meaning of statistical significance at the .05 level: “This means that it is 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance.”

That interpretation commits an egregious logical error (technical term: “transposed conditional”): confusing the odds of getting a result (if a hypothesis is true) with the odds favoring the hypothesis if you observe that result. A well-fed dog may seldom bark, but observing the rare bark does not imply that the dog is hungry. A dog may bark 5 percent of the time even if it is well-fed all of the time. (See Box 2)

Another common error equates statistical significance to “significance” in the ordinary use of the word. Because of the way statistical formulas work, a study with a very large sample can detect “statistical significance” for a small effect that is meaningless in practical terms. A new drug may be statistically better than an old drug, but for every thousand people you treat you might get just one or two additional cures — not clinically significant. Similarly, when studies claim that a chemical causes a “significantly increased risk of cancer,” they often mean that it is just statistically significant, possibly posing only a tiny absolute increase in risk.

Statisticians perpetually caution against mistaking statistical significance for practical importance, but scientific papers commit that error often. Ziliak studied journals from various fields — psychology, medicine and economics among others — and reported frequent disregard for the distinction.

“I found that eight or nine of every 10 articles published in the leading journals make the fatal substitution” of equating statistical significance to importance, he said in an interview. Ziliak’s data are documented in the 2008 book The Cult of Statistical Significance, coauthored with Deirdre McCloskey of the University of Illinois at Chicago.

Multiplicity of mistakesEven when “significance” is properly defined and P values are carefully calculated, statistical inference is plagued by many other problems. Chief among them is the “multiplicity” issue — the testing of many hypotheses simultaneously. When several drugs are tested at once, or a single drug is tested on several groups, chances of getting a statistically significant but false result rise rapidly.

Recognizing these problems, some researchers now calculate a “false discovery rate” to warn of flukes disguised as real effects. And genetics researchers have begun using “genome-wide association studies” that attempt to ameliorate the multiplicity issue (SN: 6/21/08, p. 20).

Many researchers now also commonly report results with confidence intervals, similar to the margins of error reported in opinion polls. Such intervals, usually given as a range that should include the actual value with 95 percent confidence, do convey a better sense of how precise a finding is. But the 95 percent confidence calculation is based on the same math as the .05 P value and so still shares some of its problems.

Statistical problems also afflict the “gold standard” for medical research, the randomized, controlled clinical trials that test drugs for their ability to cure or their power to harm. Such trials assign patients at random to receive either the substance being tested or a placebo, typically a sugar pill; random selection supposedly guarantees that patients’ personal characteristics won’t bias the choice of who gets the actual treatment. But in practice, selection biases may still occur, Vance Berger and Sherri Weinstein noted in 2004 in ControlledClinical Trials. “Some of the benefits ascribed to randomization, for example that it eliminates all selection bias, can better be described as fantasy than reality,” they wrote.

Randomization also should ensure that unknown differences among individuals are mixed in roughly the same proportions in the groups being tested. But statistics do not guarantee an equal distribution any more than they prohibit 10 heads in a row when flipping a penny. With thousands of clinical trials in progress, some will not be well randomized. And DNA differs at more than a million spots in the human genetic catalog, so even in a single trial differences may not be evenly mixed. In a sufficiently large trial, unrandomized factors may balance out, if some have positive effects and some are negative. (See Box 3) Still, trial results are reported as averages that may obscure individual differences, masking beneficial or harm­ful effects and possibly leading to approval of drugs that are deadly for some and denial of effective treatment to others.

nother concern is the common strategy of combining results from many trials into a single “meta-analysis,” a study of studies. In a single trial with relatively few participants, statistical tests may not detect small but real and possibly important effects. In principle, combining smaller studies to create a larger sample would allow the tests to detect such small effects. But statistical techniques for doing so are valid only if certain criteria are met. For one thing, all the studies conducted on the drug must be included — published and unpublished. And all the studies should have been performed in a similar way, using the same protocols, definitions, types of patients and doses. When combining studies with differences, it is necessary first to show that those differences would not affect the analysis, Goodman notes, but that seldom happens. “That’s not a formal part of most meta-analyses,” he says.

Meta-analyses have produced many controversial conclusions. Common claims that antidepressants work no better than placebos, for example, are based on meta-analyses that do not conform to the criteria that would confer validity. Similar problems afflicted a 2007 meta-analysis, published in the New England Journal of Medicine, that attributed increased heart attack risk to the diabetes drug Avandia. Raw data from the combined trials showed that only 55 people in 10,000 had heart attacks when using Avandia, compared with 59 people per 10,000 in comparison groups. But after a series of statistical manipulations, Avandia appeared to confer an increased risk.

combining small studies in a meta-analysis is not a good substitute for a single trial sufficiently large to test a given question. “Meta-analyses can reduce the role of chance in the interpretation but may introduce bias and confounding,” Hennekens and DeMets write in the Dec. 2 Journal of the American Medical Association. “Such results should be considered more as hypothesis formulating than as hypothesis testing.”

Some studies show dramatic effects that don’t require sophisticated statistics to interpret. If the P value is 0.0001 — a hundredth of a percent chance of a fluke — that is strong evidence, Goodman points out. Besides, most well-accepted science is based not on any single study, but on studies that have been confirmed by repetition. Any one result may be likely to be wrong, but confidence rises quickly if that result is independently replicated.“Replication is vital,” says statistician Juliet Shaffer, a lecturer emeritus at the University of California, Berkeley. And in medicine, she says, the need for replication is widely recognized. “But in the social sciences and behavioral sciences, replication is not common,” she noted in San Diego in February at the annual meeting of the American Association for the Advancement of Science. “This is a sad situation.”

Most critics of standard statistics advocate the Bayesian approach to statistical reasoning, a methodology that derives from a theorem credited to Bayes, an 18th century English clergyman. His approach uses similar math, but requires the added twist of a “prior probability” — in essence, an informed guess about the expected probability of something in advance of the study. Often this prior probability is more than a mere guess — it could be based, for instance, on previous studies.

it basically just reflects the need to include previous knowledge when drawing conclusions from new observations. To infer the odds that a barking dog is hungry, for instance, it is not enough to know how often the dog barks when well-fed. You also need to know how often it eats — in order to calculate the prior probability of being hungry. Bayesian math combines a prior probability with observed data to produce an estimate of the likelihood of the hunger hypothesis. “A scientific hypothesis cannot be properly assessed solely by reference to the observational data,” but only by viewing the data in light of prior belief in the hypothesis, wrote George Diamond and Sanjay Kaul of UCLA’s School of Medicine in 2004 in the Journal of the American College of Cardiology. “Bayes’ theorem is ... a logically consistent, mathematically valid, and intuitive way to draw inferences about the hypothesis.” (See Box 4)

In many real-life contexts, Bayesian methods do produce the best answers to important questions. In medical diagnoses, for instance, the likelihood that a test for a disease is correct depends on the prevalence of the disease in the population, a factor that Bayesian math would take into account.

But Bayesian methods introduce a confusion into the actual meaning of the mathematical concept of “probability” in the real world. Standard or “frequentist” statistics treat probabilities as objective realities; Bayesians treat probabilities as “degrees of belief” based in part on a personal assessment or subjective decision about what to include in the calculation. That’s a tough placebo to swallow for scientists wedded to the “objective” ideal of standard statistics. “Subjective prior beliefs are anathema to the frequentist, who relies instead on a series of ad hoc algorithms that maintain the facade of scientific objectivity,” Diamond and Kaul wrote.Conflict between frequentists and Bayesians has been ongoing for two centuries. So science’s marriage to mathematics seems to entail some irreconcilable differences. Whether the future holds a fruitful reconciliation or an ugly separation may depend on forging a shared understanding of probability.“What does probability mean in real life?” the statistician David Salsburg asked in his 2001 book The Lady Tasting Tea. “This problem is still unsolved, and ... if it remains un­solved, the whole of the statistical approach to science may come crashing down from the weight of its own inconsistencies.”

Interestingly, all media outlets appear particularly susceptible to PR material disseminated by supermarkets: the Mail appears to have a particular appetite for publicity from Asda and Tesco, while the Guardian favours Waitrose releases.

Moore said one unexpected discovery has been that the BBC news website appears particularly prone to churning publicity material."Part of the reason is presumably because they feel a duty to put out so many government pronouncements," Moore said. "But the BBC also has a lot to produce in regions that the newspapers don't cover."

it is not quite true, even among tenured professors in the humanities, that idea-mongers can entirely ignore "external" checks. Even academics want to be respectable, which means they can't entirely ignore the realities that others notice. There were lots of academics talking about the achievements of socialism in the 1970s (I can remember them) but very few talking that way after China and Russia repudiated these fantasies.

THE MOST DISTORTING ASPECT of Sowell's account is that, in focusing so much on the delusions of intellectuals, he leaves us more confused about what motivates the rest of society. In a characteristic passage, Sowell protests that "intellectuals...have sought to replace the groups into which people have sorted themselves with groupings created and imposed by the intelligentsia. Ties of family, religion, and patriotism, for example, have long been rated as suspect or detrimental by the intelligentsia, and new ties that intellectuals have created, such as class -- and more recently 'gender' -- have been projected as either more real or more important."

There's no disputing the claim that most "intellectuals" -- surely most professors in the humanities-are down on "patriotism" and "religion" and probably even "family." But how did people get to be patriotic and religious in the first place? In Sowell's account, they just "sorted themselves" -- as if by the invisible hand of the market.

Let's put aside all the violence and intimidation that went into building so many nations and so many faiths in the past. What is it, even today, that makes people revere this country (or some other); what makes people adhere to a particular faith or church? Don't inspiring words often move people? And those who arrange these words -- aren't they doing something similar to what Sowell says intellectuals do? Is it really true, when it comes to embracing national or religious loyalties, that "the proof of the pudding is in the eating"?

Even when it comes to commercial products, people don't always want to be guided by mundane considerations of reliable performance. People like glamour, prestige, associations between the product and things they otherwise admire. That's why companies spend so much on advertising. And that's part of the reason people are willing to pay more for brand names -- to enjoy the associations generated by advertising. Even advertising plays on assumptions about what is admirable and enticing-assumptions that may change from decade to decade, as background opinions change. How many products now flaunt themselves as "green" -- and how many did so 20 years ago?

If we closed down universities and stopped subsidizing intellectual publications, would people really judge every proposed policy by external results? Intellectuals tend to see what they expect to see, as Sowell's examples show -- but that's true of almost everyone. We have background notions about how the world works that help us make sense of what we experience. We might have distorted and confused notions, but we don't just perceive isolated facts. People can improve in their understanding, developing background understandings that are more defined or more reliable. That's part of what makes people interested in the ideas of intellectuals -- the hope of improving their own understanding.

On Sowell's account, we wouldn't need the contributions of a Friedrich Hayek -- or a Thomas Sowell -- if we didn't have so many intellectuals peddling so many wrong-headed ideas. But the wealthier the society, the more it liberates individuals to make different choices and the more it can afford to indulge even wasteful or foolish choices. I'd say that means not that we have less need of intellectuals, but more need of better ones. 

When we ask whether human beings are altruistic, we want to know about their motives or intentions. Biological altruism explains how unselfish behavior might have evolved but, as Frans de Waal suggested in his column in The Stone on Sunday, it implies nothing about the motives or intentions of the agent: after all, birds and bats and bees can act altruistically. This fact helps to explain why, despite these evolutionary theories, the view that people never intentionally act to benefit others except to obtain some good for themselves still possesses a powerful lure over our thinking.

The lure of this view — egoism — has two sources, one psychological, the other logical. Consider first the psychological. One reason people deny that altruism exists is that, looking inward, they doubt the purity of their own motives. We know that even when we appear to act unselfishly, other reasons for our behavior often rear their heads: the prospect of a future favor, the boost to reputation, or simply the good feeling that comes from appearing to act unselfishly. As Kant and Freud observed, people’s true motives may be hidden, even (or perhaps especially) from themselves. Even if we think we’re acting solely to further another person’s good, that might not be the real reason. (There might be no single “real reason” — actions can have multiple motives.)

So the psychological lure of egoism as a theory of human action is partly explained by a certain humility or skepticism people have about their own or others’ motives

There’s also a less flattering reason: denying the possibility of pure altruism provides a convenient excuse for selfish behavior.

The logical lure of egoism is different: the view seems impossible to disprove. No matter how altruistic a person appears to be, it’s possible to conceive of her motive in egoistic terms.

The impossibility of disproving egoism may sound like a virtue of the theory, but, as philosophers of science know, it’s really a fatal drawback. A theory that purports to tell us something about the world, as egoism does, should be falsifiable. Not false, of course, but capable of being tested and thus proved false. If every state of affairs is compatible with egoism, then egoism doesn’t tell us anything distinctive about how things are.

s ambiguity in the concepts of desire and the satisfaction of desire. If people possess altruistic motives, then they sometimes act to benefit others without the prospect of gain to themselves. In other words, they desire the good of others for its own sake, not simply as a means to their own satisfaction.

Still, when our desires are satisfied we normally experience satisfaction; we feel good when we do good. But that doesn’t mean we do good only in order to get that “warm glow” — that our true incentives are self-interested (as economists tend to claim). Indeed, as de Waal argues, if we didn’t desire the good of others for its own sake, then attaining it wouldn’t produce the warm glow.

Common sense tells us that some people are more altruistic than others. Egoism’s claim that these differences are illusory — that deep down, everybody acts only to further their own interests — contradicts our observations and deep-seated human practices of moral evaluation.

At the same time, we may notice that generous people don’t necessarily suffer more or flourish less than those who are more self-interested.

The point is rather that the kind of altruism we ought to encourage, and probably the only kind with staying power, is satisfying to those who practice it. Studies of rescuers show that they don’t believe their behavior is extraordinary; they feel they must do what they do, because it’s just part of who they are. The same holds for more common, less newsworthy acts — working in soup kitchens, taking pets to people in nursing homes, helping strangers find their way, being neighborly. People who act in these ways believe that they ought to help others, but they also want to help, because doing so affirms who they are and want to be and the kind of world they want to exist. As Prof. Neera Badhwar has argued, their identity is tied up with their values, thus tying self-interest and altruism together. The correlation between doing good and feeling good is not inevitable— inevitability lands us again with that empty, unfalsifiable egoism — but it is more than incidental.

Altruists should not be confused with people who automatically sacrifice their own interests for others.

After questioning on suspicion of committing a section five racial public order offence, her mother Nicola says she was placed in a bare cell for three-and-a-half hours then released without charge.

"Codie was not being racist." "The reaction from the school and police is totally over the top and I am furious my daughter had to go through this trauma when all she was saying was common sense. " "She'd have been better off not saying anything and getting into trouble for not being able to do the work."

School insiders acknowledge that at least three of the students Codie refused to sit with had recently arrived in this country and spoke little English. But they say her comments afterwards raised further concerns, for example allegedly referring to the students as "blacks" - something she denied yesterday.

Last night Robert Whelan, deputy director of the Civitas think-tank, said: "It's obviously common sense that pupils who don't speak English cause problems for other pupils and for teachers." "I'm sure this sort of thing happens all the time, but it's a sad reflection on the school if they can't deal with it without involving the police." "A lot of these arrests don't result in prosecutions - they aim is to frighten us into self-censorship until we watch everything we say."

it mixes eminent common sense and frequent good reporting with a cocksure hubris utterly inappropriate to the practice it apotheosizes.

According to Pigliucci, both Freudian psychoanalysis and Marxist theory of history "are too broad, too flexible with regard to observations, to actually tell us anything interesting." (That's right—not one "interesting" thing.) The idea of intelligent design in biology "has made no progress since its last serious articulation by natural theologian William Paley in 1802," and the empirical evidence for evolution is like that for "an open-and-shut murder case."

Pigliucci offers more hero sandwiches spiced with derision and certainty. Media coverage of science is "characterized by allegedly serious journalists who behave like comedians." Commenting on the highly publicized Dover, Pa., court case in which U.S. District Judge John E. Jones III ruled that intelligent-design theory is not science, Pigliucci labels the need for that judgment a "bizarre" consequence of the local school board's "inane" resolution. Noting the complaint of intelligent-design advocate William Buckingham that an approved science textbook didn't give creationism a fair shake, Pigliucci writes, "This is like complaining that a textbook in astronomy is too focused on the Copernican theory of the structure of the solar system and unfairly neglects the possibility that the Flying Spaghetti Monster is really pulling each planet's strings, unseen by the deluded scientists."

Or is it possible that the alternate view unfairly neglected could be more like that of Harvard scientist Owen Gingerich, who contends in God's Universe (Harvard University Press, 2006) that it is partly statistical arguments—the extraordinary unlikelihood eons ago of the physical conditions necessary for self-conscious life—that support his belief in a universe "congenially designed for the existence of intelligent, self-reflective life"?

Even if we agree that capital "I" and "D" intelligent-design of the scriptural sort—what Gingerich himself calls "primitive scriptural literalism"—is not scientifically credible, does that make Gingerich's assertion, "I believe in intelligent design, lowercase i and lowercase d," equivalent to Flying-Spaghetti-Monsterism? Tone matters. And sarcasm is not science.

The problem with polemicists like Pigliucci is that a chasm has opened up between two groups that might loosely be distinguished as "philosophers of science" and "science warriors."

Philosophers of science, often operating under the aegis of Thomas Kuhn, recognize that science is a diverse, social enterprise that has changed over time, developed different methodologies in different subsciences, and often advanced by taking putative pseudoscience seriously, as in debunking cold fusion

The science warriors, by contrast, often write as if our science of the moment is isomorphic with knowledge of an objective world-in-itself—Kant be damned!—and any form of inquiry that doesn't fit the writer's criteria of proper science must be banished as "bunk." Pigliucci, typically, hasn't much sympathy for radical philosophies of science. He calls the work of Paul Feyerabend "lunacy," deems Bruno Latour "a fool," and observes that "the great pronouncements of feminist science have fallen as flat as the similarly empty utterances of supporters of intelligent design."

It doesn't have to be this way. The noble enterprise of submitting nonscientific knowledge claims to critical scrutiny—an activity continuous with both philosophy and science—took off in an admirable way in the late 20th century when Paul Kurtz, of the University at Buffalo, established the Committee for the Scientific Investigation of Claims of the Paranormal (Csicop) in May 1976. Csicop soon after launched the marvelous journal Skeptical Inquirer

Although Pigliucci himself publishes in Skeptical Inquirer, his contributions there exhibit his signature smugness. For an antidote to Pigliucci's overweening scientism 'tude, it's refreshing to consult Kurtz's curtain-raising essay, "Science and the Public," in Science Under Siege (Prometheus Books, 2009, edited by Frazier)

Kurtz's commandment might be stated, "Don't mock or ridicule—investigate and explain." He writes: "We attempted to make it clear that we were interested in fair and impartial inquiry, that we were not dogmatic or closed-minded, and that skepticism did not imply a priori rejection of any reasonable claim. Indeed, I insisted that our skepticism was not totalistic or nihilistic about paranormal claims."

Kurtz combines the ethos of both critical investigator and philosopher of science. Describing modern science as a practice in which "hypotheses and theories are based upon rigorous methods of empirical investigation, experimental confirmation, and replication," he notes: "One must be prepared to overthrow an entire theoretical framework—and this has happened often in the history of science ... skeptical doubt is an integral part of the method of science, and scientists should be prepared to question received scientific doctrines and reject them in the light of new evidence."

Pigliucci, alas, allows his animus against the nonscientific to pull him away from sensitive distinctions among various sciences to sloppy arguments one didn't see in such earlier works of science patriotism as Carl Sagan's The Demon-Haunted World: Science as a Candle in the Dark (Random House, 1995). Indeed, he probably sets a world record for misuse of the word "fallacy."

To his credit, Pigliucci at times acknowledges the nondogmatic spine of science. He concedes that "science is characterized by a fuzzy borderline with other types of inquiry that may or may not one day become sciences." Science, he admits, "actually refers to a rather heterogeneous family of activities, not to a single and universal method." He rightly warns that some pseudoscience—for example, denial of HIV-AIDS causation—is dangerous and terrible.

But at other points, Pigliucci ferociously attacks opponents like the most unreflective science fanatic

He dismisses Feyerabend's view that "science is a religion" as simply "preposterous," even though he elsewhere admits that "methodological naturalism"—the commitment of all scientists to reject "supernatural" explanations—is itself not an empirically verifiable principle or fact, but rather an almost Kantian precondition of scientific knowledge. An article of faith, some cold-eyed Feyerabend fans might say.

He writes, "ID is not a scientific theory at all because there is no empirical observation that can possibly contradict it. Anything we observe in nature could, in principle, be attributed to an unspecified intelligent designer who works in mysterious ways." But earlier in the book, he correctly argues against Karl Popper that susceptibility to falsification cannot be the sole criterion of science, because science also confirms. It is, in principle, possible that an empirical observation could confirm intelligent design—i.e., that magic moment when the ultimate UFO lands with representatives of the intergalactic society that planted early life here, and we accept their evidence that they did it.

"As long as we do not venture to make hypotheses about who the designer is and why and how she operates," he writes, "there are no empirical constraints on the 'theory' at all. Anything goes, and therefore nothing holds, because a theory that 'explains' everything really explains nothing."

Here, Pigliucci again mixes up what's likely or provable with what's logically possible or rational. The creation stories of traditional religions and scriptures do, in effect, offer hypotheses, or claims, about who the designer is—e.g., see the Bible.

Far from explaining nothing because it explains everything, such an explanation explains a lot by explaining everything. It just doesn't explain it convincingly to a scientist with other evidentiary standards.

A sensible person can side with scientists on what's true, but not with Pigliucci on what's rational and possible. Pigliucci occasionally recognizes that. Late in his book, he concedes that "nonscientific claims may be true and still not qualify as science." But if that's so, and we care about truth, why exalt science to the degree he does? If there's really a heaven, and science can't (yet?) detect it, so much the worse for science.

Pigliucci quotes a line from Aristotle: "It is the mark of an educated mind to be able to entertain a thought without accepting it." Science warriors such as Pigliucci, or Michael Ruse in his recent clash with other philosophers in these pages, should reflect on a related modern sense of "entertain." One does not entertain a guest by mocking, deriding, and abusing the guest. Similarly, one does not entertain a thought or approach to knowledge by ridiculing it.

Long live Skeptical Inquirer! But can we deep-six the egomania and unearned arrogance of the science patriots? As Descartes, that immortal hero of scientists and skeptics everywhere, pointed out, true skepticism, like true charity, begins at home.

Carlin Romano, critic at large for The Chronicle Review, teaches philosophy and media theory at the University of Pennsylvania.

Delayed gratification illustrates the interaction between the two modes. The brain is attracted by immediate rewards, no matter what kind. However, when larger rewards are eventually going to become available, other parts of the brain come into play to override (sometimes) the immediate urge.

Greene's research shows that our automatic setting is "Kantian," meaning that our intuitive responses are deontological, rule driven. The manual setting, on the other hand, tends to be more utilitarian / consequentialist. Accordingly, the first mode involves emotional areas of the brain, the second one involves more cognitive areas.

The evidence comes from the (in)famous trolley dilemma and it's many variations.

when people refuse to intervene in the footbridge (as opposed to the lever) version of the dilemma, they do so because of a strong emotional response, which contradicts the otherwise utilitarian calculus they make when considering the lever version.

psychopaths turn out to be more utilitarian than normal subjects - presumably not because consequentialism is inherently pathological, but because their emotional responses are stunted. Mood also affects the results, with people exposed to comedy (to enhance mood), for instance, more likely to say that it is okay to push the guy off the footbridge.

In a more recent experiment, subjects were asked to say which action carried the better consequences, which made them feel worse, and which was overall morally acceptable. The idea was to separate the cognitive, emotional and integrative aspects of moral decision making. Predictably, activity in the amygdala correlated with deontological judgment, activity in more cognitive areas was associated with utilitarianism, and different brain regions became involved in integrating the two.

Another recent experiment used visual vs. verbal descriptions of moral dilemmas. Turns out that more visual people tend to behave emotionally / deontologically, while more verbal people are more utilitarian.

studies show that interfering with moral judgment by engaging subjects with a cognitive task slows down (though it does not reverse) utilitarian judgment, but has no effect on deontological judgment. Again, in agreement with the conclusion that the first type of modality is the result of cognition, the latter of emotion.

Nice to know, by the way, that when experimenters controlled for "real world expectations" that people have about trolleys, or when they used more realistic scenarios than trolleys and bridges, the results don't vary. In other words, trolley thought experiments are actually informative, contrary to popular criticisms.

What factors affect people's decision making in moral judgment? The main one is proximity, with people feeling much stronger obligations if they are present to the event posing the dilemma, or even relatively near (a disaster happens in a nearby country), as opposed to when they are far (a country on the other side of the world).

Greene's general conclusion is that neuroscience matters to ethics because it reveals the hidden mechanisms of human moral decision making. However, he says this is interesting to philosophers because it may lead to question ethical theories that are implicitly or explicitly based on such judgments. But neither philosophical deontology nor consequentialism are in fact based on common moral judgments, seems to me. They are the result of explicit analysis. (Though Greene raises the possibility that some philosophers engage in rationalizing, rather than reason, as in Kant's famously convoluted idea that masturbation is wrong because one is using oneself as a mean to an end...)

this is not to say that understanding moral decision making in humans isn't interesting or in fact even helpful in real life cases. An example of the latter is the common moral condemnation of incest, which is an emotional reaction that probably evolved to avoid genetically diseased offspring. It follows that science can tell us that three is nothing morally wrong in cases of incest when precautions have been taken to avoid pregnancy (and assuming psychological reactions are also accounted for). Greene puts this in terms of science helping us to transform difficult ought questions into easier ought questions.

But now all sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants: Davis has a forthcoming analysis demonstrating that the efficacy of antidepressants has gone down as much as threefold in recent decades.

the effect is especially troubling because of what it exposes about the scientific process. If replication is what separates the rigor of science from the squishiness of pseudoscience, where do we put all these rigorously validated findings that can no longer be proved? Which results should we believe? Francis Bacon, the early-modern philosopher and pioneer of the scientific method, once declared that experiments were essential, because they allowed us to “put nature to the question.” But it appears that nature often gives us different answers.

At first, he assumed that he’d made an error in experimental design or a statistical miscalculation. But he couldn’t find anything wrong with his research. He then concluded that his initial batch of research subjects must have been unusually susceptible to verbal overshadowing. (John Davis, similarly, has speculated that part of the drop-off in the effectiveness of antipsychotics can be attributed to using subjects who suffer from milder forms of psychosis which are less likely to show dramatic improvement.) “It wasn’t a very satisfying explanation,” Schooler says. “One of my mentors told me that my real mistake was trying to replicate my work. He told me doing that was just setting myself up for disappointment.”

In private, Schooler began referring to the problem as “cosmic habituation,” by analogy to the decrease in response that occurs when individuals habituate to particular stimuli. “Habituation is why you don’t notice the stuff that’s always there,” Schooler says. “It’s an inevitable process of adjustment, a ratcheting down of excitement. I started joking that it was like the cosmos was habituating to my ideas. I took it very personally.”

The most likely explanation for the decline is an obvious one: regression to the mean. As the experiment is repeated, that is, an early statistical fluke gets cancelled out. The extrasensory powers of Schooler’s subjects didn’t decline—they were simply an illusion that vanished over time. And yet Schooler has noticed that many of the data sets that end up declining seem statistically solid—that is, they contain enough data that any regression to the mean shouldn’t be dramatic. “These are the results that pass all the tests,” he says. “The odds of them being random are typically quite remote, like one in a million. This means that the decline effect should almost never happen. But it happens all the time!

this is why Schooler believes that the decline effect deserves more attention: its ubiquity seems to violate the laws of statistics. “Whenever I start talking about this, scientists get very nervous,” he says. “But I still want to know what happened to my results. Like most scientists, I assumed that it would get easier to document my effect over time. I’d get better at doing the experiments, at zeroing in on the conditions that produce verbal overshadowing. So why did the opposite happen? I’m convinced that we can use the tools of science to figure this out. First, though, we have to admit that we’ve got a problem.”

In 2001, Michael Jennions, a biologist at the Australian National University, set out to analyze “temporal trends” across a wide range of subjects in ecology and evolutionary biology. He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance. In fact, even when numerous variables were controlled for—Jennions knew, for instance, that the same author might publish several critical papers, which could distort his analysis—there was still a significant decrease in the validity of the hypothesis, often within a year of publication. Jennions admits that his findings are troubling, but expresses a reluctance to talk about them publicly. “This is a very sensitive issue for scientists,” he says. “You know, we’re supposed to be dealing with hard facts, the stuff that’s supposed to stand the test of time. But when you see these trends you become a little more skeptical of things.”

the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data. It was too exciting an idea to disprove, at least back then.

the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.

Jennions, similarly, argues that the decline effect is largely a product of publication bias, or the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for. A “significant” result is defined as any data point that would be produced by chance less than five per cent of the time. This ubiquitous test was invented in 1922 by the English mathematician Ronald Fisher, who picked five per cent as the boundary line, somewhat arbitrarily, because it made pencil and slide-rule calculations easier. Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments. In recent years, publication bias has mostly been seen as a problem for clinical trials, since pharmaceutical companies are less interested in publishing results that aren’t favorable. But it’s becoming increasingly clear that publication bias also produces major distortions in fields without large corporate incentives, such as psychology and ecology.

While publication bias almost certainly plays a role in the decline effect, it remains an incomplete explanation. For one thing, it fails to account for the initial prevalence of positive results among studies that never even get submitted to journals. It also fails to explain the experience of people like Schooler, who have been unable to replicate their initial data despite their best efforts

an equally significant issue is the selective reporting of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.

The funnel graph visually captures the distortions of selective reporting. For instance, after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results.

Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting is everywhere in science, I got quite depressed,” Palmer told me. “As a researcher, you’re always aware that there might be some nonrandom patterns, but I had no idea how widespread it is.” In a recent review article, Palmer summarized the impact of selective reporting on his field: “We cannot escape the troubling conclusion that some—perhaps many—cherished generalities are at best exaggerated in their biological significance and at worst a collective illusion nurtured by strong a-priori beliefs often repeated.”

Palmer emphasizes that selective reporting is not the same as scientific fraud. Rather, the problem seems to be one of subtle omissions and unconscious misperceptions, as researchers struggle to make sense of their results. Stephen Jay Gould referred to this as the “shoehorning” process. “A lot of scientific measurement is really hard,” Simmons told me. “If you’re talking about fluctuating asymmetry, then it’s a matter of minuscule differences between the right and left sides of an animal. It’s millimetres of a tail feather. And so maybe a researcher knows that he’s measuring a good male”—an animal that has successfully mated—“and he knows that it’s supposed to be symmetrical. Well, that act of measurement is going to be vulnerable to all sorts of perception biases. That’s not a cynical statement. That’s just the way human beings work.”

One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials. Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits. As Palmer notes, this wide discrepancy suggests that scientists find ways to confirm their preferred hypothesis, disregarding what they don’t want to see. Our beliefs are a form of blindness.

John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.”

In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals. Forty-five of these studies reported positive results, suggesting that the intervention being tested was effective. Because most of these studies were randomized controlled trials—the “gold standard” of medical evidence—they tended to have a significant impact on clinical practice, and led to the spread of treatments such as hormone replacement therapy for menopausal women and daily low-dose aspirin to prevent heart attacks and strokes. Nevertheless, the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.

The situation is even worse when a subject is fashionable. In recent years, for instance, there have been hundreds of studies on the various genes that control the differences in disease risk between men and women. These findings have included everything from the mutations responsible for the increased risk of schizophrenia to the genes underlying hypertension. Ioannidis and his colleagues looked at four hundred and thirty-two of these claims. They quickly discovered that the vast majority had serious flaws. But the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”

the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher. “The scientists are so eager to pass this magical test that they start playing around with the numbers, trying to find anything that seems worthy,” Ioannidis says. In recent years, Ioannidis has become increasingly blunt about the pervasiveness of the problem. One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”

The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies that show a strong effect. They really want to believe that it’s true.”

scientists need to become more rigorous about data collection before they publish. “We’re wasting too much time chasing after bad studies and underpowered experiments,” he says. The current “obsession” with replicability distracts from the real problem, which is faulty design. He notes that nobody even tries to replicate most science papers—there are simply too many. (According to Nature, a third of all studies never even get cited, let alone repeated.)

Schooler recommends the establishment of an open-source database, in which researchers are required to outline their planned investigations and document all their results. “I think this would provide a huge increase in access to scientific work and give us a much better way to judge the quality of an experiment,” Schooler says. “It would help us finally deal with all these issues that the decline effect is exposing.”

Although such reforms would mitigate the dangers of publication bias and selective reporting, they still wouldn’t erase the decline effect. This is largely because scientific research will always be shadowed by a force that can’t be curbed, only contained: sheer randomness. Although little research has been done on the experimental dangers of chance and happenstance, the research that exists isn’t encouraging

John Crabbe, a neuroscientist at the Oregon Health and Science University, conducted an experiment that showed how unknowable chance events can skew tests of replicability. He performed a series of experiments on mouse behavior in three different science labs: in Albany, New York; Edmonton, Alberta; and Portland, Oregon. Before he conducted the experiments, he tried to standardize every variable he could think of. The same strains of mice were used in each lab, shipped on the same day from the same supplier. The animals were raised in the same kind of enclosure, with the same brand of sawdust bedding. They had been exposed to the same amount of incandescent light, were living with the same number of littermates, and were fed the exact same type of chow pellets. When the mice were handled, it was with the same kind of surgical glove, and when they were tested it was on the same equipment, at the same time in the morning.

The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results. “If any set of experiments should have passed the test, it should have been ours,” Crabbe says. “But that’s not the way it turned out.” In one experiment, Crabbe injected a particular strain of mouse with cocaine. In Portland the mice given the drug moved, on average, six hundred centimetres more than they normally did; in Albany they moved seven hundred and one additional centimetres. But in the Edmonton lab they moved more than five thousand additional centimetres. Similar deviations were observed in a test of anxiety. Furthermore, these inconsistencies didn’t follow any detectable pattern. In Portland one strain of mouse proved most anxious, while in Albany another strain won that distinction.

The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise. The hyperactivity of those coked-up Edmonton mice wasn’t an interesting new fact—it was a meaningless outlier, a by-product of invisible variables we don’t understand. The problem, of course, is that such dramatic findings are also the most likely to get published in prestigious journals, since the data are both statistically significant and entirely unexpected. Grants get written, follow-up studies are conducted. The end result is a scientific accident that can take years to unravel.

This suggests that the decline effect is actually a decline of illusion.

While Karl Popper imagined falsification occurring with a single, definitive experiment—Galileo refuted Aristotelian mechanics in an afternoon—the process turns out to be much messier than that. Many scientific theories continue to be considered true even after failing numerous experimental tests. Verbal overshadowing might exhibit the decline effect, but it remains extensively relied upon within the field. The same holds for any number of phenomena, from the disappearing benefits of second-generation antipsychotics to the weak coupling ratio exhibited by decaying neutrons, which appears to have fallen by more than ten standard deviations between 1969 and 2001. Even the law of gravity hasn’t always been perfect at predicting real-world phenomena. (In one test, physicists measuring gravity by means of deep boreholes in the Nevada desert found a two-and-a-half-per-cent discrepancy between the theoretical predictions and the actual data.) Despite these findings, second-generation antipsychotics are still widely prescribed, and our model of the neutron hasn’t changed. The law of gravity remains the same.

Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling. Not because it reveals the human fallibility of science, in which data are tweaked and beliefs shape perceptions. (Such shortcomings aren’t surprising, at least for scientists.) And not because it reveals that many of our most exciting theories are fleeting fads and will soon be rejected. (That idea has been around since Thomas Kuhn.) The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that’s often not the case. Just because an idea is true doesn’t mean it can be proved. And just because an idea can be proved doesn’t mean it’s true. When the experiments are done, we still have to choose what to believe.