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the first obvious serious objection is that technological “evolution” is in no logical way a continuation of biological evolution — the word “evolution” here being applied with completely different meanings. And besides, there is no scientifically sensible way in which biological evolution has been accelerating over the several billion years of its operation on our planet. So much for scientific accuracy and logical consistency.

here is a bit that will give you an idea of why some people think of Singularitarianism as a secular religion: “The Singularity will allow us to transcend [the] limitations of our biological bodies and brains. We will gain power over our fates. Our mortality will be in our own hands. We will be able to live as long as we want.”

Fig. 2 of that essay shows a progression through (again, entirely arbitrary) six “epochs,” with the next one (#5) occurring when there will be a merger between technological and human intelligence (somehow, a good thing), and the last one (#6) labeled as nothing less than “the universe wakes up” — a nonsensical outcome further described as “patterns of matter and energy in the universe becom[ing] saturated with intelligence processes and knowledge.” This isn’t just science fiction, it is bad science fiction.

“a serious assessment of the history of technology reveals that technological change is exponential. Exponential growth is a feature of any evolutionary process.” First, it is highly questionable that one can even measure “technological change” on a coherent uniform scale. Yes, we can plot the rate of, say, increase in microprocessor speed, but that is but one aspect of “technological change.” As for the idea that any evolutionary process features exponential growth, I don’t know where Kurzweil got it, but it is simply wrong, for one thing because biological evolution does not have any such feature — as any student of Biology 101 ought to know.

Kurzweil’s ignorance of evolution is manifested again a bit later, when he claims — without argument, as usual — that “Evolution is a process of creating patterns of increasing order. ... It’s the evolution of patterns that constitutes the ultimate story of the world. ... Each stage or epoch uses the information-processing methods of the previous epoch to create the next.” I swear, I was fully expecting a scholarly reference to Deepak Chopra at the end of that sentence. Again, “evolution” is a highly heterogeneous term that picks completely different concepts, such as cosmic “evolution” (actually just change over time), biological evolution (which does have to do with the creation of order, but not in Kurzweil’s blatantly teleological sense), and technological “evolution” (which is certainly yet another type of beast altogether, since it requires intelligent design). And what on earth does it mean that each epoch uses the “methods” of the previous one to “create” the next one?

As we have seen, the whole idea is that human beings will merge with machines during the ongoing process of ever accelerating evolution, an event that will eventually lead to the universe awakening to itself, or something like that. Now here is the crucial question: how come this has not happened already?

To appreciate the power of this argument you may want to refresh your memory about the Fermi Paradox, a serious (though in that case, not a knockdown) argument against the possibility of extraterrestrial intelligent life. The story goes that physicist Enrico Fermi (the inventor of the first nuclear reactor) was having lunch with some colleagues, back in 1950. His companions were waxing poetic about the possibility, indeed the high likelihood, that the galaxy is teeming with intelligent life forms. To which Fermi asked something along the lines of: “Well, where are they, then?”

The idea is that even under very pessimistic (i.e., very un-Kurzweil like) expectations about how quickly an intelligent civilization would spread across the galaxy (without even violating the speed of light limit!), and given the mind boggling length of time the galaxy has already existed, it becomes difficult (though, again, not impossible) to explain why we haven’t seen the darn aliens yet.

Now, translate that to Kurzweil’s much more optimistic predictions about the Singularity (which allegedly will occur around 2045, conveniently just a bit after Kurzweil’s expected demise, given that he is 63 at the time of this writing). Considering that there is no particular reason to think that planet earth, or the human species, has to be the one destined to trigger the big event, why is it that the universe hasn’t already “awakened” as a result of a Singularity occurring somewhere else at some other time?

1. The harm principle bases our ethical considerations on other beings’ capacity for higher-level subjective experience. Human beings (and some animals) have the potential — and desire — to experience deep pleasure and happiness while seeking to avoid pain and suffering. We have the obligation, then, to afford creatures with these capacities, desires and relations a certain level of respect. They also have other emotional and social interests: for instance, friends and families concerned with their health and enjoyment. These actors also deserve consideration.

2. If we have a moral obligation to act a certain way toward someone, that should be reflected in law. Rights theory is the idea that there are certain rights worth granting to people with very few, if any, caveats. Many of these rights were spelled out in the founding documents of this country, the Declaration of Independence (which admittedly has no legal pull) and the Constitution (which does). They have been defended in a long history of U.S. Supreme Court rulings. They have also been expanded on in the U.N.’s 1948 Universal Declaration of Human Rights and in the founding documents of other countries around the world. To name a few, they include: freedom of belief, speech and expression, due process, equal treatment, health care, and education.

3. While we ought to consider our broader moral efforts, and focus on our obligations to others, it is also important to place attention on our quality as moral agents. A vital part of fostering a respectable pluralist moral framework is to encourage virtues, and cultivate moral character. A short list of these virtues would include prudence, justice, wisdom, honesty, compassion, and courage. One should study these, and strive to put these into practice and work to be a better human being, as Aristotle advised us to do.

most people already are ethical pluralists. Life and society are complex to navigate, and one cannot rely on a single idea for guidance. It is probably accurate to say that people lean more toward one theory, rather than practice it to the exclusion of all others. Of course, this only describes the fact that people think about morality in a pluralistic way. But the outlined approach is supported, sound reasoning — that is, unless you are ready to entirely dismiss 2,500 years of Western moral philosophy.

how long should political liberals have let debate last? Here we reach another issue to parse, that of dividing the spheres of discourse of politics and society.The political sphere includes lawmakers, who have their name for a reason: they make laws. They cannot sit around and debate endlessly. They must, at some point, push legislation through (which is at the center of the debate over filibuster reform).

take note of another passage from Mill: “It is the duty of governments, and of individuals, to form the truest opinions they can; to form them carefully, and never impose them on others unless they are quite sure of being right. But when they are sure … it is not conscientiousness but cowardice to shrink from acting on their opinions. ... Men, and governments, must act to the best of their ability. There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life." (Mill, 25-26, emphasis added).

Yet our division of spheres of discourse means passage of a bill – or even defeat – does not mark the end of debate. Indeed, many Americans continued to discuss the merits of the legislation, with some even filing lawsuits arguing it was unconstitutional (I think these stand little chance of going anywhere). American society at large can and will continue to have the conversation about health insurance reform. Then, in the next election, they will bring their beliefs to the polls. They will expect those voted in to act. And then, the conversation will continue. Politics is a continuous process. By dividing up spheres of discourse into political and societal, we see that debate never really ends – it’s just that sometimes lawmakers need to get on with their job, and leave debate to the public.

Constraining imagination by knowledge does not make it redundant. We rarely know an explicit formula that tells us what to do in a complex situation. We have to work out what to do by thinking through the possibilities in ways that are simultaneously imaginative and realistic, and not less imaginative when more realistic. Knowledge, far from limiting imagination, enables it to serve its central function.

we can borrow a distinction from the philosophy of science, between contexts of discovery and contexts of justification. In the context of discovery, we get ideas, no matter how — dreams or drugs will do. Then, in the context of justification, we assemble objective evidence to determine whether the ideas are correct. On this picture, standards of rationality apply only to the context of justification, not to the context of discovery. Those who downplay the cognitive role of the imagination restrict it to the context of discovery, excluding it from the context of justification. But they are wrong. Imagination plays a vital role in justifying ideas as well as generating them in the first place.

Second of all, fields naturally select for people who accept their foundational assumptions. People who don't accept those assumptions are likely not to have gone into that field in the first place, or to have left it already.

Sometimes those foundational assumptions are simple enough that an outsider can evaluate them - for instance, I may not be an expert in astrology or theology, but I can understand their starting premises (stars affect human fates; we should accept the Bible as the truth) well enough to confidently dismiss them, and the fields that rest on them. But when the foundational assumptions get more complex - like the assumption that we can reliably model future temperatures - it becomes much harder for an outsider to judge their soundness.

we almost seem to be stuck in a Catch-22: The only people who are qualified to evaluate the validity of a complex field are the ones who have studied that field in depth - in other words, experts. Yet the experts are also the people who have the strongest incentives not to reject the foundational assumptions of the field, and the ones who have self-selected for believing those assumptions. So the closer you are to a field, the more biased you are, which makes you a poor judge of it; the farther away you are, the less relevant knowledge you have, which makes you a poor judge of it. What to do?

luckily, the Catch-22 isn't quite as stark as I made it sound. For example, you can often find people who are experts in the particular methodology used by a field without actually being a member of the field, so they can be much more unbiased judges of whether that field is applying the methodology soundly. So for example, a foundational principle underlying a lot of empirical social science research is that linear regression is a valid tool for modeling most phenomena. I strongly recommend asking a statistics professor about that. 

there are some general criteria that outsiders can use to evaluate the validity of a technical field, even without “technical scientific expertise” in that field. For example, can the field make testable predictions, and does it have a good track record of predicting things correctly? This seems like a good criterion by which an outsider can judge the field of climate modeling (and "predictions" here includes using your model to predict past data accurately). I don't need to know how the insanely-complicated models work to know that successful prediction is a good sign.

And there are other more field-specific criteria outsiders can often use. For example, I've barely studied postmodernism at all, but I don't have to know much about the field to recognize that the fact that they borrow concepts from complex disciplines which they themselves haven't studied is a red flag.

the issue with AGW is less the science and all about the political solutions. Most every solution we hear in the public conversation requires some level of sacrifice and uncertainty in the future.Politicians, neither experts in climatology nor economics, craft legislation to solve the problem through the lens of their own political ideology. At TAM8, this was pretty apparent. My honest opinion is that people who are AGW skeptics are mainly skeptics of the political solutions. If AGW was said to increase the GDP of the country by two to three times, I'm guessing you'd see a lot less climate change skeptics.

This illustrates, Bloom says, that people have a rudimentary sense of justice from a very early age. This doesn’t make people naturally good. If you give a 3-year-old two pieces of candy and ask him if he wants to share one of them, he will almost certainly say no. It’s not until age 7 or 8 that even half the children are willing to share. But it does mean that social norms fall upon prepared ground. We come equipped to learn fairness and other virtues.

If you ask for donations with the photo and name of one sick child, you are likely to get twice as much money than if you had asked for donations with a photo and the names of eight children. Our minds respond more powerfully to the plight of an individual than the plight of a group.

If you are in a bad mood you will make harsher moral judgments than if you’re in a good mood or have just seen a comedy. As Elizabeth Phelps of New York University points out, feelings of disgust will evoke a desire to expel things, even those things unrelated to your original mood. General fear makes people risk-averse. Anger makes them risk-seeking.

People who behave morally don’t generally do it because they have greater knowledge; they do it because they have a greater sensitivity to other people’s points of view.

The moral naturalists differ over what role reason plays in moral judgments. Some, like Haidt, believe that we make moral judgments intuitively and then construct justifications after the fact. Others, like Joshua Greene of Harvard, liken moral thinking to a camera. Most of the time we rely on the automatic point-and-shoot process, but occasionally we use deliberation to override the quick and easy method.

For people wary of abstract theorizing, it’s nice to see people investigating morality in ways that are concrete and empirical. But their approach does have certain implicit tendencies. They emphasize group cohesion over individual dissent. They emphasize the cooperative virtues, like empathy, over the competitive virtues, like the thirst for recognition and superiority. At this conference, they barely mentioned the yearning for transcendence and the sacred, which plays such a major role in every human society. Their implied description of the moral life is gentle, fair and grounded. But it is all lower case. So far, at least, it might not satisfy those who want their morality to be awesome, formidable, transcendent or great.

Choice, free or coerced, is neither a sufficient nor a necessary condition for responsibility.

All that is required to be responsible for an event is to be in the causal chain leading to an event.

In his final book, Power and Prosperity, the late Mancur Olson argued that the state was a “stationary bandit”. A stationary bandit is better than a “roving bandit”, because the latter has no interest in developing the economy, while the former does. But it may not be much better, because those who control the state will seek to extract the surplus over subsistence generated by those under their control.

In the contemporary west, there are three protections against undue exploitation by the stationary bandit: exit, voice (on the first two of these, see this on Albert Hirschman) and restraint. By “exit”, I mean the possibility of escaping from the control of a given jurisdiction, by emigration, capital flight or some form of market exchange. By “voice”, I mean a degree of control over, the state, most obviously by voting. By “restraint”, I mean independent courts, division of powers, federalism and entrenched rights.

defining what a democratic state, viewed precisely as such a constrained protective arrangement, is entitled to do.

There exists a strand in classical liberal or, in contemporary US parlance, libertarian thought which believes the answer is to define the role of the state so narrowly and the rights of individuals so broadly that many political choices (the income tax or universal health care, for example) would be ruled out a priori. In other words, it seeks to abolish much of politics through constitutional restraints. I view this as a hopeless strategy, both intellectually and politically. It is hopeless intellectually, because the values people hold are many and divergent and some of these values do not merely allow, but demand, government protection of weak, vulnerable or unfortunate people. Moreover, such values are not “wrong”. The reality is that people hold many, often incompatible, core values. Libertarians argue that the only relevant wrong is coercion by the state. Others disagree and are entitled to do so. It is hopeless politically, because democracy necessitates debate among widely divergent opinions. Trying to rule out a vast range of values from the political sphere by constitutional means will fail. Under enough pressure, the constitution itself will be changed, via amendment or reinterpretation.

So what ought the protective role of the state to include? Again, in such a discussion, classical liberals would argue for the “night-watchman” role. The government’s responsibilities are limited to protecting individuals from coercion, fraud and theft and to defending the country from foreign aggression. Yet once one has accepted the legitimacy of using coercion (taxation) to provide the goods listed above, there is no reason in principle why one should not accept it for the provision of other goods that cannot be provided as well, or at all, by non-political means.

Those other measures would include addressing a range of externalities (e.g. pollution), providing information and supplying insurance against otherwise uninsurable risks, such as unemployment, spousal abandonment and so forth. The subsidisation or public provision of childcare and education is a way to promote equality of opportunity. The subsidisation or public provision of health insurance is a way to preserve life, unquestionably one of the purposes of the state. Safety standards are a way to protect people against the carelessness or malevolence of others or (more controversially) themselves. All these, then, are legitimate protective measures. The more complex the society and economy, the greater the range of the protections that will be sought.

What, then, are the objections to such actions? The answers might be: the proposed measures are ineffective, compared with what would happen in the absence of state intervention; the measures are unaffordable and might lead to state bankruptcy; the measures encourage irresponsible behaviour; and, at the limit, the measures restrict individual autonomy to an unacceptable degree. These are all, we should note, questions of consequences.

The vote is more evenly distributed than wealth and income. Thus, one would expect the tenor of democratic policymaking to be redistributive and so, indeed, it is. Those with wealth and income to protect will then make political power expensive to acquire and encourage potential supporters to focus on common enemies (inside and outside the country) and on cultural values. The more unequal are incomes and wealth and the more determined are the “haves” to avoid being compelled to support the “have-nots”, the more politics will take on such characteristics.

In the 1970s, the view that democracy would collapse under the weight of its excessive promises seemed to me disturbingly true. I am no longer convinced of this: as Adam Smith said, “There is a great deal of ruin in a nation”. Moreover, the capacity for learning by democracies is greater than I had realised. The conservative movements of the 1980s were part of that learning. But they went too far in their confidence in market arrangements and their indifference to the social and political consequences of inequality. I would support state pensions, state-funded health insurance and state regulation of environmental and other externalities. I am happy to debate details. The ancient Athenians called someone who had a purely private life “idiotes”. This is, of course, the origin of our word “idiot”. Individual liberty does indeed matter. But it is not the only thing that matters. The market is a remarkable social institution. But it is far from perfect. Democratic politics can be destructive. But it is much better than the alternatives. Each of us has an obligation, as a citizen, to make politics work as well as he (or she) can and to embrace the debate over a wide range of difficult choices that this entails.

The people with this attitude aren't just fringe fundamentalists who are fearful of messing with God's Plan. Many of them are prestigious professors and authors whose arguments make no mention of religion. One of the most prominent examples is political theorist Francis Fukuyama, author of End of History, who published a book in 2003 called “Our Posthuman Future: Consequences of the Biotechnology Revolution.” In it he argues that we will lose our “essential” humanity by enhancing ourselves, and that the result will be a loss of respect for “human dignity” and a collapse of morality.

Fukuyama's reasoning represents a prominent strain of thought about human enhancement, and one that I find doubly fallacious. (Fukuyama is aware of the following criticisms, but neither I nor other reviewers were impressed by his attempt to defend himself against them.) The idea that the status quo represents some “essential” quality of humanity collapses when you zoom out and look at the steady change in the human condition over previous millennia. Our ancestors were less knowledgable, more tribalistic, less healthy, shorter-lived; would Fukuyama have argued for the preservation of all those qualities on the grounds that, in their respective time, they constituted an “essential human nature”? And even if there were such a thing as a persistent “human nature,” why is it necessarily worth preserving? In other words, I would argue that Fukuyama is committing both the fallacy of essentialism (there exists a distinct thing that is “human nature”) and the appeal to nature (the way things naturally are is how they ought to be).

Writer Bill McKibben, who was called “probably the nation's leading environmentalist” by the Boston Globe this year, and “the world's best green journalist” by Time magazine, published a book in 2003 called “Enough: Staying Human in an Engineered Age.” In it he writes, “That is the choice... one that no human should have to make... To be launched into a future without bounds, where meaning may evaporate.” McKibben concludes that it is likely that “meaning and pain, meaning and transience are inextricably intertwined.” Or as one blogger tartly paraphrased: “If we all live long healthy happy lives, Bill’s favorite poetry will become obsolete.”

President George W. Bush's Council on Bioethics, which advised him from 2001-2009, was steeped in it. Harvard professor of political philosophy Michael J. Sandel served on the Council from 2002-2005 and penned an article in the Atlantic Monthly called “The Case Against Perfection,” in which he objected to genetic engineering on the grounds that, basically, it’s uppity. He argues that genetic engineering is “the ultimate expression of our resolve to see ourselves astride the world, the masters of our nature.” Better we should be bowing in submission than standing in mastery, Sandel feels. Mastery “threatens to banish our appreciation of life as a gift,” he warns, and submitting to forces outside our control “restrains our tendency toward hubris.”

If you like Sandel's “It's uppity” argument against human enhancement, you'll love his fellow Councilmember Dr. William Hurlbut's argument against life extension: “It's unmanly.” Hurlbut's exact words, delivered in a 2007 debate with Aubrey de Grey: “I actually find a preoccupation with anti-aging technologies to be, I think, somewhat spiritually immature and unmanly... I’m inclined to think that there’s something profound about aging and death.”

And Council chairman Dr. Leon Kass, a professor of bioethics from the University of Chicago who served from 2001-2005, was arguably the worst of all. Like McKibben, Kass has frequently argued against radical life extension on the grounds that life's transience is central to its meaningfulness. “Could the beauty of flowers depend on the fact that they will soon wither?” he once asked. “How deeply could one deathless ‘human’ being love another?”

Kass has also argued against human enhancements on the same grounds as Fukuyama, that we shouldn't deviate from our proper nature as human beings. “To turn a man into a cockroach— as we don’t need Kafka to show us —would be dehumanizing. To try to turn a man into more than a man might be so as well,” he said. And Kass completes the anti-transhumanist triad (it robs life of meaning; it's dehumanizing; it's hubris) by echoing Sandel's call for humility and gratitude, urging, “We need a particular regard and respect for the special gift that is our own given nature.”

By now you may have noticed a familiar ring to a lot of this language. The idea that it's virtuous to suffer, and to humbly surrender control of your own fate, is a cornerstone of Christian morality.

it's fairly representative of standard Christian tropes: surrendering to God, submitting to God, trusting that God has good reasons for your suffering.

I suppose I can understand that if you believe in an all-powerful entity who will become irate if he thinks you are ungrateful for anything, then this kind of groveling might seem like a smart strategic move. But what I can't understand is adopting these same attitudes in the absence of any religious context. When secular people chastise each other for the “hubris” of trying to improve the “gift” of life they've received, I want to ask them: just who, exactly, are you groveling to? Who, exactly, are you afraid of affronting if you dare to reach for better things?

This is why transhumanism is most needed, from my perspective – to counter the astoundingly widespread attitude that suffering and 80-year-lifespans are good things that are worth preserving. That attitude may make sense conditional on certain peculiarly masochistic theologies, but the rest of us have no need to defer to it. It also may have been a comforting thing to tell ourselves back when we had no hope of remedying our situation, but that's not necessarily the case anymore.

I think there is a seperation of Transhumanism and what Massimo is referring to. Things like robotic arms and the like come from trying to deal with a specific defect and thus seperate it from Transhumanism. I would define transhumanism the same way you would (the achievement of a better human), but I would exclude the inventions of many life altering devices as transhumanism. If we could invent a device that just made you smarter, then ideed that would be transhumanism, but if we invented a device that could make someone that was metally challenged to be able to be normal, I would define this as modern medicine. I just want to make sure we seperate advances in modern medicine from transhumanism. Modern medicine being the one that advances to deal with specific medical issues to improve quality of life (usually to restore it to normal conditions) and transhumanism being the one that can advance every single human (perhaps equally?).

I agree with all your points about why the arguments against transhumanism and for suffering are ridiculous. That being said, when I first heard about the ideas of Transhumanism, after the initial excitement wore off (since I'm a big tech nerd), my reaction was more of less the same as Massimo's. I don't particularly see the need for a philosophical movement for this.

if people believe that suffering is something God ordained for us, you're not going to convince them otherwise with philosophical arguments any more than you'll convince them there's no God at all. If the technologies do develop, acceptance of them will come as their use becomes more prevalent, not with arguments.

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.

Science, then, has its limits, but when dealing with claims that are testable within the methods of science, such as medical claims, to demand scientific evidence is simply to hold everyone to the same standards as the scientific community holds itself to. To cry scientism in this case shows disregard for the scientific method.

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.”

human rights were discussed—when they were mentioned at all—as demands made in particular times and places. Some of these demands might be universal in scope—that torture be prohibited everywhere was frequently (though not always) formulated in terms of an all-encompassing necessity, but no one imagined that human rights comprised the only possible universal morality.

the notion that rights are the foundation of society came only with the rise of the Harvard philosopher John Rawls’s vastly influential A Theory of Justice (1971). In the years following, it slowly came to be accepted that human rights were the bottom line in political morality.