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Suppose I concede to Prinz that reason cannot settle differences in moral values and sentiments. Difference of opinion doesn’t mean that there isn’t a true or rational answer. In fact, there are many reasons why our cognition, emotional reactions or previous values could be wrong or irrational — and why people would not pick up on their deficiencies. In his article, Prinz uses the case of sociopaths, who simply lack certain cognitive abilities. There are many reasons other than sociopathy why human beings can get things wrong, morally speaking, often and badly. It could be that people are unable to adopt a more objective morality because of their circumstances — from brain deficiencies to lack of access to relevant information. But, again, none of this amounts to an argument against the existence of objective morality.

As it turns out, Prinz’s conception of objective morality does not quite reflect the thinking of most people who believe in objective morality. He writes that: “Objectivism holds that there is one true morality binding upon all of us.” This is a particular strand of moral realism, but there are many. For instance, one can judge some moral precepts as better than others, yet remain open to the fact that there are probably many different ways to establish a good society. This is a pluralistic conception of objective morality which doesn’t assume one absolute moral truth. For all that has been said, Sam Harris’ idea of a moral landscape does help illustrate this concept. Thinking in terms of better and worse morality gets us out of relativism and into an objectivist approach. The important thing to note is that one need not go all the way to absolute objectivity to work toward a rational, non-arbitrary morality.

even Prinz admits that “Relativism does not entail that we should tolerate murderous tyranny. When someone threatens us or our way of life, we are strongly motivated to protect ourselves.” That is, there are such things as better and worse values: the worse ones kill us, the better ones don’t. This is a very broad criterion, but it is an objective standard. Prinz is arguing for a tighter moral relativism – a sort of stripped down objective morality that is constricted by nature, experience, and our (modest) reasoning abilities.

I proposed at the discussion that a more objective morality could be had with the help of a robust public discourse on the issues at hand. Prinz does not necessarily disagree. He wrote that “Many people have overlapping moral values, and one can settle debates by appeal to moral common ground.” But Prinz pointed out a couple of limitations on public discourse. For example, the agreements we reach on “moral common ground” are often exclusive of some, and abstract in content. Consider the United Nations Declaration of Human Rights, a seemingly good example of global moral agreement. Yet, it was ratified by a small sample of 48 countries, and it is based on suspiciously Western sounding language. Everyone has a right to education and health care, but — Prinz pointed out during the discussion — what level of education and health care? Still, the U.N. declaration was passed 48-0 with just 8 abstentions (Belarus, Czechoslovakia, Poland, Ukraine, USSR, Yugoslavia, South Africa and Saudi Arabia). It includes 30 articles of ethical standards agreed upon by 48 countries around the world. Such a document does give us more reason to think that public discourse can lead to significant agreement upon values.

Reason might not be able to arrive at moral truths, but it can push us to test and question the rationality of our values — a crucial part in the process that leads to the adoption of new, or modified values. The only way to reduce disputes about morality is to try to get people on the same page about their moral goals. Given the above, this will not be easy, and perhaps we shouldn’t be too optimistic in our ability to employ reason to figure things out. But reason is still the best, and even only, tool we can wield, and while it might not provide us with a truly objective morality, it’s enough to save us from complete moral relativism.

But the problem is deeper than simply intellectual inertia. It goes back, ultimately, to the unconsidered differentiations we make—at every moment when we distinguish among objects—between those in the foreground of our consciousness and the background places in which the objects happen to be situated. Moreover, this distinction creates a hierarchy of objects. We are conscious not only of the skin that encloses and defines the object, but of bits and pieces of that object, each of which must have its own “skin.” That is the problem of anatomization. A car has a motor and brakes and a transmission and an outer body that, at appropriate moments, become separate objects of our consciousness, objects that at least some knowledgeable person recognizes as coherent entities.

Evelyn Fox Keller sees “The Mirage of a Space Between Nature and Nurture” as a consequence of our false division of the world into living objects without sufficient consideration of the external milieu in which they are embedded, since organisms help create effective environments through their own life activities.

The central point of her analysis has been that gender itself (as opposed to sex) is socially constructed, and that construction has influenced the development of science:If there is a single point on which all feminist scholarship…has converged, it is the importance of recognizing the social construction of gender…. All of my work on gender and science proceeds from this basic recognition. My endeavor has been to call attention to the ways in which the social construction of a binary opposition between “masculine” and “feminine” has influenced the social construction of science.

major critical concern of Fox Keller’s present book is the widespread attempt to partition in some quantitative way the contribution made to human variation by differences in biological inheritance, that is, differences in genes, as opposed to differences in life experience. She wants to make clear a distinction between analyzing the relative strength of the causes of variation among individuals and groups, an analysis that is coherent in principle, and simply assigning the relative contributions of biological and environmental causes to the value of some character in an individual

It is, for example, all very well to say that genetic variation is responsible for 76 percent of the observed variation in adult height among American women while the remaining 24 percent is a consequence of differences in nutrition. The implication is that if all variation in nutrition were abolished then 24 percent of the observed height variation among individuals in the population in the next generation would disappear. To say, however, that 76 percent of Evelyn Fox Keller’s height was caused by her genes and 24 percent by her nutrition does not make sense. The nonsensical implication of trying to partition the causes of her individual height would be that if she never ate anything she would still be three quarters as tall as she is.

In fact, Keller is too optimistic about the assignment of causes of variation even when considering variation in a population. As she herself notes parenthetically, the assignment of relative proportions of population variation to different causes in a population depends on there being no specific interaction between the causes.

Keller’s rather casual treatment of the interaction between causal factors in the case of the drummers, despite her very great sophistication in analyzing the meaning of variation, is a symptom of a fault that is deeply embedded in the analytic training and thinking of both natural and social scientists. If there are several variable factors influencing some phenomenon, how are we to assign the relative importance to each in determining total variation? Let us take an extreme example. Suppose that we plant seeds of each of two different varieties of corn in two different locations with the following results measured in bushels of corn produced (see Table 1). There are differences between the varieties in their yield from location to location and there are differences between locations from variety to variety. So, both variety and location matter. But there is no average variation between locations when averaged over varieties or between varieties when averaged over locations. Just by knowing the variation in yield associated with location and variety separately does not tell us which factor is the more important source of variation; nor do the facts of location and variety exhaust the description of that variation.

Unlike the drum language, which was based on spoken language, the optical telegraph was based on written French. Chappe invented an elaborate coding system to translate written messages into optical signals. Chappe had the opposite problem from the drummers. The drummers had a fast transmission system with ambiguous messages. They needed to slow down the transmission to make the messages unambiguous. Chappe had a painfully slow transmission system with redundant messages. The French language, like most alphabetic languages, is highly redundant, using many more letters than are needed to convey the meaning of a message. Chappe’s coding system allowed messages to be transmitted faster. Many common phrases and proper names were encoded by only two optical symbols, with a substantial gain in speed of transmission. The composer and the reader of the message had code books listing the message codes for eight thousand phrases and names. For Napoleon it was an advantage to have a code that was effectively cryptographic, keeping the content of the messages secret from citizens along the route.

After these two historical examples of rapid communication in Africa and France, the rest of Gleick’s book is about the modern development of information technolog

The modern history is dominated by two Americans, Samuel Morse and Claude Shannon. Samuel Morse was the inventor of Morse Code. He was also one of the pioneers who built a telegraph system using electricity conducted through wires instead of optical pointers deployed on towers. Morse launched his electric telegraph in 1838 and perfected the code in 1844. His code used short and long pulses of electric current to represent letters of the alphabet.

Morse was ideologically at the opposite pole from Chappe. He was not interested in secrecy or in creating an instrument of government power. The Morse system was designed to be a profit-making enterprise, fast and cheap and available to everybody. At the beginning the price of a message was a quarter of a cent per letter. The most important users of the system were newspaper correspondents spreading news of local events to readers all over the world. Morse Code was simple enough that anyone could learn it. The system provided no secrecy to the users. If users wanted secrecy, they could invent their own secret codes and encipher their messages themselves. The price of a message in cipher was higher than the price of a message in plain text, because the telegraph operators could transcribe plain text faster. It was much easier to correct errors in plain text than in cipher.

Claude Shannon was the founding father of information theory. For a hundred years after the electric telegraph, other communication systems such as the telephone, radio, and television were invented and developed by engineers without any need for higher mathematics. Then Shannon supplied the theory to understand all of these systems together, defining information as an abstract quantity inherent in a telephone message or a television picture. Shannon brought higher mathematics into the game.

When Shannon was a boy growing up on a farm in Michigan, he built a homemade telegraph system using Morse Code. Messages were transmitted to friends on neighboring farms, using the barbed wire of their fences to conduct electric signals. When World War II began, Shannon became one of the pioneers of scientific cryptography, working on the high-level cryptographic telephone system that allowed Roosevelt and Churchill to talk to each other over a secure channel. Shannon’s friend Alan Turing was also working as a cryptographer at the same time, in the famous British Enigma project that successfully deciphered German military codes. The two pioneers met frequently when Turing visited New York in 1943, but they belonged to separate secret worlds and could not exchange ideas about cryptography.

In 1945 Shannon wrote a paper, “A Mathematical Theory of Cryptography,” which was stamped SECRET and never saw the light of day. He published in 1948 an expurgated version of the 1945 paper with the title “A Mathematical Theory of Communication.” The 1948 version appeared in the Bell System Technical Journal, the house journal of the Bell Telephone Laboratories, and became an instant classic. It is the founding document for the modern science of information. After Shannon, the technology of information raced ahead, with electronic computers, digital cameras, the Internet, and the World Wide Web.

According to Gleick, the impact of information on human affairs came in three installments: first the history, the thousands of years during which people created and exchanged information without the concept of measuring it; second the theory, first formulated by Shannon; third the flood, in which we now live

The event that made the flood plainly visible occurred in 1965, when Gordon Moore stated Moore’s Law. Moore was an electrical engineer, founder of the Intel Corporation, a company that manufactured components for computers and other electronic gadgets. His law said that the price of electronic components would decrease and their numbers would increase by a factor of two every eighteen months. This implied that the price would decrease and the numbers would increase by a factor of a hundred every decade. Moore’s prediction of continued growth has turned out to be astonishingly accurate during the forty-five years since he announced it. In these four and a half decades, the price has decreased and the numbers have increased by a factor of a billion, nine powers of ten. Nine powers of ten are enough to turn a trickle into a flood.

Gordon Moore was in the hardware business, making hardware components for electronic machines, and he stated his law as a law of growth for hardware. But the law applies also to the information that the hardware is designed to embody. The purpose of the hardware is to store and process information. The storage of information is called memory, and the processing of information is called computing. The consequence of Moore’s Law for information is that the price of memory and computing decreases and the available amount of memory and computing increases by a factor of a hundred every decade. The flood of hardware becomes a flood of information.

In 1949, one year after Shannon published the rules of information theory, he drew up a table of the various stores of memory that then existed. The biggest memory in his table was the US Library of Congress, which he estimated to contain one hundred trillion bits of information. That was at the time a fair guess at the sum total of recorded human knowledge. Today a memory disc drive storing that amount of information weighs a few pounds and can be bought for about a thousand dollars. Information, otherwise known as data, pours into memories of that size or larger, in government and business offices and scientific laboratories all over the world. Gleick quotes the computer scientist Jaron Lanier describing the effect of the flood: “It’s as if you kneel to plant the seed of a tree and it grows so fast that it swallows your whole town before you can even rise to your feet.”

On December 8, 2010, Gleick published on the The New York Review’s blog an illuminating essay, “The Information Palace.” It was written too late to be included in his book. It describes the historical changes of meaning of the word “information,” as recorded in the latest quarterly online revision of the Oxford English Dictionary. The word first appears in 1386 a parliamentary report with the meaning “denunciation.” The history ends with the modern usage, “information fatigue,” defined as “apathy, indifference or mental exhaustion arising from exposure to too much information.”

The consequences of the information flood are not all bad. One of the creative enterprises made possible by the flood is Wikipedia, started ten years ago by Jimmy Wales. Among my friends and acquaintances, everybody distrusts Wikipedia and everybody uses it. Distrust and productive use are not incompatible. Wikipedia is the ultimate open source repository of information. Everyone is free to read it and everyone is free to write it. It contains articles in 262 languages written by several million authors. The information that it contains is totally unreliable and surprisingly accurate. It is often unreliable because many of the authors are ignorant or careless. It is often accurate because the articles are edited and corrected by readers who are better informed than the authors

Jimmy Wales hoped when he started Wikipedia that the combination of enthusiastic volunteer writers with open source information technology would cause a revolution in human access to knowledge. The rate of growth of Wikipedia exceeded his wildest dreams. Within ten years it has become the biggest storehouse of information on the planet and the noisiest battleground of conflicting opinions. It illustrates Shannon’s law of reliable communication. Shannon’s law says that accurate transmission of information is possible in a communication system with a high level of noise. Even in the noisiest system, errors can be reliably corrected and accurate information transmitted, provided that the transmission is sufficiently redundant. That is, in a nutshell, how Wikipedia works.

The information flood has also brought enormous benefits to science. The public has a distorted view of science, because children are taught in school that science is a collection of firmly established truths. In fact, science is not a collection of truths. It is a continuing exploration of mysteries. Wherever we go exploring in the world around us, we find mysteries. Our planet is covered by continents and oceans whose origin we cannot explain. Our atmosphere is constantly stirred by poorly understood disturbances that we call weather and climate. The visible matter in the universe is outweighed by a much larger quantity of dark invisible matter that we do not understand at all. The origin of life is a total mystery, and so is the existence of human consciousness. We have no clear idea how the electrical discharges occurring in nerve cells in our brains are connected with our feelings and desires and actions.

Even physics, the most exact and most firmly established branch of science, is still full of mysteries. We do not know how much of Shannon’s theory of information will remain valid when quantum devices replace classical electric circuits as the carriers of information. Quantum devices may be made of single atoms or microscopic magnetic circuits. All that we know for sure is that they can theoretically do certain jobs that are beyond the reach of classical devices. Quantum computing is still an unexplored mystery on the frontier of information theory. Science is the sum total of a great multitude of mysteries. It is an unending argument between a great multitude of voices. It resembles Wikipedia much more than it resembles the Encyclopaedia Britannica.

The rapid growth of the flood of information in the last ten years made Wikipedia possible, and the same flood made twenty-first-century science possible. Twenty-first-century science is dominated by huge stores of information that we call databases. The information flood has made it easy and cheap to build databases. One example of a twenty-first-century database is the collection of genome sequences of living creatures belonging to various species from microbes to humans. Each genome contains the complete genetic information that shaped the creature to which it belongs. The genome data-base is rapidly growing and is available for scientists all over the world to explore. Its origin can be traced to the year 1939, when Shannon wrote his Ph.D. thesis with the title “An Algebra for Theoretical Genetics.

Shannon was then a graduate student in the mathematics department at MIT. He was only dimly aware of the possible physical embodiment of genetic information. The true physical embodiment of the genome is the double helix structure of DNA molecules, discovered by Francis Crick and James Watson fourteen years later. In 1939 Shannon understood that the basis of genetics must be information, and that the information must be coded in some abstract algebra independent of its physical embodiment. Without any knowledge of the double helix, he could not hope to guess the detailed structure of the genetic code. He could only imagine that in some distant future the genetic information would be decoded and collected in a giant database that would define the total diversity of living creatures. It took only sixty years for his dream to come true.

In the twentieth century, genomes of humans and other species were laboriously decoded and translated into sequences of letters in computer memories. The decoding and translation became cheaper and faster as time went on, the price decreasing and the speed increasing according to Moore’s Law. The first human genome took fifteen years to decode and cost about a billion dollars. Now a human genome can be decoded in a few weeks and costs a few thousand dollars. Around the year 2000, a turning point was reached, when it became cheaper to produce genetic information than to understand it. Now we can pass a piece of human DNA through a machine and rapidly read out the genetic information, but we cannot read out the meaning of the information. We shall not fully understand the information until we understand in detail the processes of embryonic development that the DNA orchestrated to make us what we are.

The explosive growth of information in our human society is a part of the slower growth of ordered structures in the evolution of life as a whole. Life has for billions of years been evolving with organisms and ecosystems embodying increasing amounts of information. The evolution of life is a part of the evolution of the universe, which also evolves with increasing amounts of information embodied in ordered structures, galaxies and stars and planetary systems. In the living and in the nonliving world, we see a growth of order, starting from the featureless and uniform gas of the early universe and producing the magnificent diversity of weird objects that we see in the sky and in the rain forest. Everywhere around us, wherever we look, we see evidence of increasing order and increasing information. The technology arising from Shannon’s discoveries is only a local acceleration of the natural growth of information.

. Lord Kelvin, one of the leading physicists of that time, promoted the heat death dogma, predicting that the flow of heat from warmer to cooler objects will result in a decrease of temperature differences everywhere, until all temperatures ultimately become equal. Life needs temperature differences, to avoid being stifled by its waste heat. So life will disappear

Thanks to the discoveries of astronomers in the twentieth century, we now know that the heat death is a myth. The heat death can never happen, and there is no paradox. The best popular account of the disappearance of the paradox is a chapter, “How Order Was Born of Chaos,” in the book Creation of the Universe, by Fang Lizhi and his wife Li Shuxian.2 Fang Lizhi is doubly famous as a leading Chinese astronomer and a leading political dissident. He is now pursuing his double career at the University of Arizona.

The belief in a heat death was based on an idea that I call the cooking rule. The cooking rule says that a piece of steak gets warmer when we put it on a hot grill. More generally, the rule says that any object gets warmer when it gains energy, and gets cooler when it loses energy. Humans have been cooking steaks for thousands of years, and nobody ever saw a steak get colder while cooking on a fire. The cooking rule is true for objects small enough for us to handle. If the cooking rule is always true, then Lord Kelvin’s argument for the heat death is correct.

the cooking rule is not true for objects of astronomical size, for which gravitation is the dominant form of energy. The sun is a familiar example. As the sun loses energy by radiation, it becomes hotter and not cooler. Since the sun is made of compressible gas squeezed by its own gravitation, loss of energy causes it to become smaller and denser, and the compression causes it to become hotter. For almost all astronomical objects, gravitation dominates, and they have the same unexpected behavior. Gravitation reverses the usual relation between energy and temperature. In the domain of astronomy, when heat flows from hotter to cooler objects, the hot objects get hotter and the cool objects get cooler. As a result, temperature differences in the astronomical universe tend to increase rather than decrease as time goes on. There is no final state of uniform temperature, and there is no heat death. Gravitation gives us a universe hospitable to life. Information and order can continue to grow for billions of years in the future, as they have evidently grown in the past.

The vision of the future as an infinite playground, with an unending sequence of mysteries to be understood by an unending sequence of players exploring an unending supply of information, is a glorious vision for scientists. Scientists find the vision attractive, since it gives them a purpose for their existence and an unending supply of jobs. The vision is less attractive to artists and writers and ordinary people. Ordinary people are more interested in friends and family than in science. Ordinary people may not welcome a future spent swimming in an unending flood of information.

A darker view of the information-dominated universe was described in a famous story, “The Library of Babel,” by Jorge Luis Borges in 1941.3 Borges imagined his library, with an infinite array of books and shelves and mirrors, as a metaphor for the universe.

Gleick’s book has an epilogue entitled “The Return of Meaning,” expressing the concerns of people who feel alienated from the prevailing scientific culture. The enormous success of information theory came from Shannon’s decision to separate information from meaning. His central dogma, “Meaning is irrelevant,” declared that information could be handled with greater freedom if it was treated as a mathematical abstraction independent of meaning. The consequence of this freedom is the flood of information in which we are drowning. The immense size of modern databases gives us a feeling of meaninglessness. Information in such quantities reminds us of Borges’s library extending infinitely in all directions. It is our task as humans to bring meaning back into this wasteland. As finite creatures who think and feel, we can create islands of meaning in the sea of information. Gleick ends his book with Borges’s image of the human condition:We walk the corridors, searching the shelves and rearranging them, looking for lines of meaning amid leagues of cacophony and incoherence, reading the history of the past and of the future, collecting our thoughts and collecting the thoughts of others, and every so often glimpsing mirrors, in which we may recognize creatures of the information.

an Object is more likely to show up in your News Feed if people you know have been interacting with it recently. That really isn’t particularly surprising. Neither is the resulting message to developers: if you want your posts to show up in News Feed, make sure people will actually want to interact with them.

Steinberg hinted that a simpler version of News Feed may be on the way, as the current two-tabbed system is a bit complicated. That said, many people still use both tabs, with over 50% of users clicking over to the ‘most recent’ tab on a regular basis.

If you want to watch the video for yourself, click here, navigate to the Techniques sessions, and click on ‘Focus on Feed’. The talk about Facebook’s algorithms begins around 22 minutes in.

he argues that these revised theories coincide with a particular form of consequentialism, which is a theory in the same broad family as utilitarianism. If Parfit is right, there is much less disagreement between apparently conflicting moral theories than we all thought. The defenders of each of these theories are, in Parfit’s vivid phrase, “climbing the same mountain on different sides.”

Parfit’s real interest is in combating subjectivism and nihilism. Unless he can show that objectivism is true, he believes, nothing matters.

When Parfit does come to the question of “what matters,” his answer might seem surprisingly obvious. He tells us, for example, that what matters most now is that “we rich people give up some of our luxuries, ceasing to overheat the Earth’s atmosphere, and taking care of this planet in other ways, so that it continues to support intelligent life.” Many of us had already reached that conclusion. What we gain from Parfit’s work is the possibility of defending these and other moral claims as objective truths.

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."

The journal will cover negative (or “secondary”) experiments coming from all disciplines of Biology (Botany, Cell Biology, Genetics, Ecology, Microbiology, etc). An article in The All Results Journals should be created to show the failed experiments tuning methods or reactions. Articles should present experimental discoveries, interpret their significance and establish perspective with respect to earlier work of the author. It is also advisable to cite the work where the experiments has already been tuned and published.

Suppose you want to say, "Bush read Chomsky's latest book." Let's focus on just the verb, "read." To say this sentence in English, we have to mark the verb for tense; in this case, we have to pronounce it like "red" and not like "reed." In Indonesian you need not (in fact, you can't) alter the verb to mark tense. In Russian you would have to alter the verb to indicate tense and gender. So if it was Laura Bush who did the reading, you'd use a different form of the verb than if it was George. In Russian you'd also have to include in the verb information about completion. If George read only part of the book, you'd use a different form of the verb than if he'd diligently plowed through the whole thing. In Turkish you'd have to include in the verb how you acquired this information: if you had witnessed this unlikely event with your own two eyes, you'd use one verb form, but if you had simply read or heard about it, or inferred it from something Bush said, you'd use a different verb form.

Clearly, languages require different things of their speakers. Does this mean that the speakers think differently about the world? Do English, Indonesian, Russian, and Turkish speakers end up attending to, partitioning, and remembering their experiences differently just because they speak different languages?

For some scholars, the answer to these questions has been an obvious yes. Just look at the way people talk, they might say. Certainly, speakers of different languages must attend to and encode strikingly different aspects of the world just so they can use their language properly. Scholars on the other side of the debate don't find the differences in how people talk convincing. All our linguistic utterances are sparse, encoding only a small part of the information we have available. Just because English speakers don't include the same information in their verbs that Russian and Turkish speakers do doesn't mean that English speakers aren't paying attention to the same things; all it means is that they're not talking about them. It's possible that everyone thinks the same way, notices the same things, but just talks differently.

Believers in cross-linguistic differences counter that everyone does not pay attention to the same things: if everyone did, one might think it would be easy to learn to speak other languages. Unfortunately, learning a new language (especially one not closely related to those you know) is never easy; it seems to require paying attention to a new set of distinctions. Whether it's distinguishing modes of being in Spanish, evidentiality in Turkish, or aspect in Russian, learning to speak these languages requires something more than just learning vocabulary: it requires paying attention to the right things in the world so that you have the correct information to include in what you say.

Follow me to Pormpuraaw, a small Aboriginal community on the western edge of Cape York, in northern Australia. I came here because of the way the locals, the Kuuk Thaayorre, talk about space. Instead of words like "right," "left," "forward," and "back," which, as commonly used in English, define space relative to an observer, the Kuuk Thaayorre, like many other Aboriginal groups, use cardinal-direction terms — north, south, east, and west — to define space.1 This is done at all scales, which means you have to say things like "There's an ant on your southeast leg" or "Move the cup to the north northwest a little bit." One obvious consequence of speaking such a language is that you have to stay oriented at all times, or else you cannot speak properly. The normal greeting in Kuuk Thaayorre is "Where are you going?" and the answer should be something like " Southsoutheast, in the middle distance." If you don't know which way you're facing, you can't even get past "Hello."

The result is a profound difference in navigational ability and spatial knowledge between speakers of languages that rely primarily on absolute reference frames (like Kuuk Thaayorre) and languages that rely on relative reference frames (like English).2 Simply put, speakers of languages like Kuuk Thaayorre are much better than English speakers at staying oriented and keeping track of where they are, even in unfamiliar landscapes or inside unfamiliar buildings. What enables them — in fact, forces them — to do this is their language. Having their attention trained in this way equips them to perform navigational feats once thought beyond human capabilities. Because space is such a fundamental domain of thought, differences in how people think about space don't end there. People rely on their spatial knowledge to build other, more complex, more abstract representations. Representations of such things as time, number, musical pitch, kinship relations, morality, and emotions have been shown to depend on how we think about space. So if the Kuuk Thaayorre think differently about space, do they also think differently about other things, like time? This is what my collaborator Alice Gaby and I came to Pormpuraaw to find out.

To test this idea, we gave people sets of pictures that showed some kind of temporal progression (e.g., pictures of a man aging, or a crocodile growing, or a banana being eaten). Their job was to arrange the shuffled photos on the ground to show the correct temporal order. We tested each person in two separate sittings, each time facing in a different cardinal direction. If you ask English speakers to do this, they'll arrange the cards so that time proceeds from left to right. Hebrew speakers will tend to lay out the cards from right to left, showing that writing direction in a language plays a role.3 So what about folks like the Kuuk Thaayorre, who don't use words like "left" and "right"? What will they do? The Kuuk Thaayorre did not arrange the cards more often from left to right than from right to left, nor more toward or away from the body. But their arrangements were not random: there was a pattern, just a different one from that of English speakers. Instead of arranging time from left to right, they arranged it from east to west. That is, when they were seated facing south, the cards went left to right. When they faced north, the cards went from right to left. When they faced east, the cards came toward the body and so on. This was true even though we never told any of our subjects which direction they faced. The Kuuk Thaayorre not only knew that already (usually much better than I did), but they also spontaneously used this spatial orientation to construct their representations of time.

I have described how languages shape the way we think about space, time, colors, and objects. Other studies have found effects of language on how people construe events, reason about causality, keep track of number, understand material substance, perceive and experience emotion, reason about other people's minds, choose to take risks, and even in the way they choose professions and spouses.8 Taken together, these results show that linguistic processes are pervasive in most fundamental domains of thought, unconsciously shaping us from the nuts and bolts of cognition and perception to our loftiest abstract notions and major life decisions. Language is central to our experience of being human, and the languages we speak profoundly shape the way we think, the way we see the world, the way we live our lives.

The fact that even quirks of grammar, such as grammatical gender, can affect our thinking is profound. Such quirks are pervasive in language; gender, for example, applies to all nouns, which means that it is affecting how people think about anything that can be designated by a noun.

How does an artist decide whether death, say, or time should be painted as a man or a woman? It turns out that in 85 percent of such personifications, whether a male or female figure is chosen is predicted by the grammatical gender of the word in the artist's native language. So, for example, German painters are more likely to paint death as a man, whereas Russian painters are more likely to paint death as a woman.

Does treating chairs as masculine and beds as feminine in the grammar make Russian speakers think of chairs as being more like men and beds as more like women in some way? It turns out that it does. In one study, we asked German and Spanish speakers to describe objects having opposite gender assignment in those two languages. The descriptions they gave differed in a way predicted by grammatical gender. For example, when asked to describe a "key" — a word that is masculine in German and feminine in Spanish — the German speakers were more likely to use words like "hard," "heavy," "jagged," "metal," "serrated," and "useful," whereas Spanish speakers were more likely to say "golden," "intricate," "little," "lovely," "shiny," and "tiny." To describe a "bridge," which is feminine in German and masculine in Spanish, the German speakers said "beautiful," "elegant," "fragile," "peaceful," "pretty," and "slender," and the Spanish speakers said "big," "dangerous," "long," "strong," "sturdy," and "towering." This was true even though all testing was done in English, a language without grammatical gender. The same pattern of results also emerged in entirely nonlinguistic tasks (e.g., rating similarity between pictures). And we can also show that it is aspects of language per se that shape how people think: teaching English speakers new grammatical gender systems influences mental representations of objects in the same way it does with German and Spanish speakers. Apparently even small flukes of grammar, like the seemingly arbitrary assignment of gender to a noun, can have an effect on people's ideas of concrete objects in the world.

Even basic aspects of time perception can be affected by language. For example, English speakers prefer to talk about duration in terms of length (e.g., "That was a short talk," "The meeting didn't take long"), while Spanish and Greek speakers prefer to talk about time in terms of amount, relying more on words like "much" "big", and "little" rather than "short" and "long" Our research into such basic cognitive abilities as estimating duration shows that speakers of different languages differ in ways predicted by the patterns of metaphors in their language. (For example, when asked to estimate duration, English speakers are more likely to be confused by distance information, estimating that a line of greater length remains on the test screen for a longer period of time, whereas Greek speakers are more likely to be confused by amount, estimating that a container that is fuller remains longer on the screen.)

An important question at this point is: Are these differences caused by language per se or by some other aspect of culture? Of course, the lives of English, Mandarin, Greek, Spanish, and Kuuk Thaayorre speakers differ in a myriad of ways. How do we know that it is language itself that creates these differences in thought and not some other aspect of their respective cultures? One way to answer this question is to teach people new ways of talking and see if that changes the way they think. In our lab, we've taught English speakers different ways of talking about time. In one such study, English speakers were taught to use size metaphors (as in Greek) to describe duration (e.g., a movie is larger than a sneeze), or vertical metaphors (as in Mandarin) to describe event order. Once the English speakers had learned to talk about time in these new ways, their cognitive performance began to resemble that of Greek or Mandarin speakers. This suggests that patterns in a language can indeed play a causal role in constructing how we think.6 In practical terms, it means that when you're learning a new language, you're not simply learning a new way of talking, you are also inadvertently learning a new way of thinking. Beyond abstract or complex domains of thought like space and time, languages also meddle in basic aspects of visual perception — our ability to distinguish colors, for example. Different languages divide up the color continuum differently: some make many more distinctions between colors than others, and the boundaries often don't line up across languages.

To test whether differences in color language lead to differences in color perception, we compared Russian and English speakers' ability to discriminate shades of blue. In Russian there is no single word that covers all the colors that English speakers call "blue." Russian makes an obligatory distinction between light blue (goluboy) and dark blue (siniy). Does this distinction mean that siniy blues look more different from goluboy blues to Russian speakers? Indeed, the data say yes. Russian speakers are quicker to distinguish two shades of blue that are called by the different names in Russian (i.e., one being siniy and the other being goluboy) than if the two fall into the same category. For English speakers, all these shades are still designated by the same word, "blue," and there are no comparable differences in reaction time. Further, the Russian advantage disappears when subjects are asked to perform a verbal interference task (reciting a string of digits) while making color judgments but not when they're asked to perform an equally difficult spatial interference task (keeping a novel visual pattern in memory). The disappearance of the advantage when performing a verbal task shows that language is normally involved in even surprisingly basic perceptual judgments — and that it is language per se that creates this difference in perception between Russian and English speakers.

What it means for a language to have grammatical gender is that words belonging to different genders get treated differently grammatically and words belonging to the same grammatical gender get treated the same grammatically. Languages can require speakers to change pronouns, adjective and verb endings, possessives, numerals, and so on, depending on the noun's gender. For example, to say something like "my chair was old" in Russian (moy stul bil' stariy), you'd need to make every word in the sentence agree in gender with "chair" (stul), which is masculine in Russian. So you'd use the masculine form of "my," "was," and "old." These are the same forms you'd use in speaking of a biological male, as in "my grandfather was old." If, instead of speaking of a chair, you were speaking of a bed (krovat'), which is feminine in Russian, or about your grandmother, you would use the feminine form of "my," "was," and "old."

the emergence of the blogosphere requires significantly more openness from scientists. However, providing the details necessary to validate large datasets can be difficult and time-consuming, and how FoI laws apply to research is still an evolving area. Meanwhile, the public needs to understand that science cannot and does not produce absolutely precise answers. Though the uncertainties may become smaller and better constrained over time, uncertainty in science is a fact of life which policymakers have to deal with. The chapter concludes: “the Review would urge all scientists to learn to communicate their work in ways that the public can access and understand”.

email is less formal than other forms of communication: “Extreme forms of language are frequently applied to quite normal situations by people who would never use it in other communication channels.” The CRU scientists assumed their emails to be private, so they used “slang, jargon and acronyms” which would have been more fully explained had they been talking to the public. And although some emails suggest CRU went out of their way to make life difficult for their critics, there are others which suggest they were bending over backwards to be honest. Therefore the Review found “the e-mails cannot always be relied upon as evidence of what actually occurred, nor indicative of actual behaviour that is extreme, exceptional or unprofessional.” [section 4.3]

when put into the proper context, what do these emails actually reveal about the behaviour of the CRU scientists? The report concluded (its emphasis):

we find that their rigour and honesty as scientists are not in doubt.

we did not find any evidence of behaviour that might undermine the conclusions of the IPCC assessments.

The argument that Climategate reveals an international climate science conspiracy is not really a very skeptical one. Sure, it is skeptical in the weak sense of questioning authority, but it stops there. Unlike true skepticism, it doesn’t go on to objectively examine all the evidence and draw a conclusion based on that evidence. Instead, it cherry-picks suggestive emails, seeing everything as incontrovertible evidence of a conspiracy, and concludes all of mainstream climate science is guilty by association. This is not skepticism; this is conspiracy theory.

The media dropped the ball There is a famous quotation attributed to Mark Twain: “A lie can travel halfway around the world while the truth is putting on its shoes.” This is more true in the internet age than it was when Mark Twain was alive. Unfortunately, it took months for the Climategate inquiries to put on their shoes, and by the time they reported, the damage had already been done. The media acted as an uncritical loudspeaker for the initial allegations, which will now continue to circulate around the world forever, then failed to give anywhere near the same amount of coverage to the inquiries clearing the scientists involved. For instance, Rupert Murdoch’s The Australian published no less than 85 stories about Climategate, but not one about the Muir Russell inquiry.

Even the Guardian, who have a relatively good track record on environmental reporting and were quick to criticize the worst excesses of climate conspiracy theorists, could not resist the lure of stolen emails. As George Monbiot writes, journalists see FoI requests and email hacking as a way of keeping people accountable, rather than the distraction from actual science which they are to scientists. In contrast, CRU director Phil Jones says: “I wish people would spend as much time reading my scientific papers as they do reading my e-mails.”

This is part of a broader problem with climate change reporting: the media holds scientists to far higher standards than it does contrarians. Climate scientists have to be right 100% of the time, but contrarians apparently can get away with being wrong nearly 100% of the time. The tiniest errors of climate scientists are nitpicked and blown out of all proportion, but contrarians get away with monstrous distortions and cherry-picking of evidence. Around the same time The Australian was bashing climate scientists, the same newspaper had no problem publishing Viscount Monckton’s blatant misrepresentations of IPCC projections (not to mention his demonstrably false conspiracy theory that the Copenhagen summit was a plot to establish a world government).

In the current model of environmental reporting, the contrarians do not lose anything by making baseless accusations. In fact, it is in their interests to throw as much mud at scientists as possible to increase the chance that some of it will stick in the public consciousness. But there is untold damage to the reputation of the scientists against whom the accusations are being made. We can only hope that in future the media will be less quick to jump to conclusions. If only editors and producers would stop and think for a moment about what they’re doing: they are playing with the future of the planet.

Cuteness is a pretty silly criterion by which to assign moral status, though. It's not as if unappealing animals are less intelligent or less sensitive to pain.

And if you have any trouble seeing the absurdity of basing moral judgments on cuteness, it helps to try out the principle in other contexts. (Is it worse to abuse a cute child than an ugly one?)

But I think the biggest reason that different examples of hurting animals for pleasure elicit different reactions from people is not about the types of animals involved, but about the types of pleasure.

One objective difference people might cite is the fact that a desire to eat meat is “natural” while a desire to watch kittens being crushed is not. Which is true, in the sense that our species did evolve to eat meat while a fetish for crushing kittens is an aberration. But using naturalness as a criterion for moral rightness is a dubious move. First, it seems rather arbitrary, from a logical perspective, which is why it's often referred to as the naturalistic fallacy. And second, it would justify some pretty unsavory “natural” urges, like rape and tribalism, while prohibiting other “unnatural” urges, like the desire to wear clothing or to refrain from having children.

The closest thing that I can find to a morally relevant distinction between industrial farming, dogfighting, and crush videos is this: While it’s true that all three acts cause animal suffering in order to give people pleasure, the nature of that tradeoff differs. The consumers of crush videos and dogfighting are taking pleasure in the suffering itself, whereas the consumers of industrially-farmed meat are taking pleasure in the meat that was produced by the suffering. From a purely harm-based perspective, the moral calculus is the same: the animal suffers so that you can experience pleasure. But the degree of directness of that tradeoff makes a difference in how we perceive your character. Someone whose motive is “I enjoy seeing another creature suffer” seems more evil than someone whose motive is “I want a tasty meal,” even if both people cause the same amount of suffering.

And I can certainly understand why people would want to call a crush video enthusiast more “evil” than a person who buys meat from industrial farms, because of the difference in their motivations. That's a reasonable way to define evilness. But in that case we're left with the fact that a person's evilness may be totally unrelated to the amount of harm she causes; and that, in fact, some of the greatest harm may be caused by people whose motivations seem unobjectionable to us. Apathy, denial, conformity; none of these inspire the same outrage as sadism, but they've caused some pretty horrible outcomes. And if you believe that it's wrong to make animals suffer for our pleasure, but you reserve your moral condemnation only for cases that viscerally upset you, like dogfighting or crush videos, then you're falling prey to the trap that Isaac Asimov famously warned us against: “Never let your sense of morals prevent you from doing what is right.”

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.”

And there is another, related aspect of discussions about eugenics that should be at the forefront of our consideration: what was particularly objectionable about American and Nazi early 20th century eugenics is that the state, not individuals, were to make decisions about who could reproduce and who couldn’t. Savulescu continues: “to grant procreative liberty is the only way to avoid the objectionable form of eugenics that the Nazis practiced.” In other words, it makes all the difference in the world if it is an individual couple who decides to have or not have a baby, or if it is the state that imposes a particular reproductive choice on its citizenry.

but then Savulescu expands his argument to a point where I begin to feel somewhat uncomfortable. He says: “[procreative liberty] involves the freedom to choose a child with red hair or blond hair or no hair.”

Savulescu has suddenly sneaked into his argument for procreative liberty the assumption that all choices in this area are on the same level. But while it is hard to object to action aimed at avoiding devastating diseases, it is not quite so obvious to me what arguments favor the idea of designer babies. The first intervention can be justified, for instance, on consequentialist grounds because it reduces the pain and suffering of both the child and the parents. The second intervention is analogous to shopping for a new bag, or a new car, which means that it commodifies the act of conceiving a baby, thus degrading its importance. I’m not saying that that in itself is sufficient to make it illegal, but the ethics of it is different, and that difference cannot simply be swept under the broad rug of “procreative liberty.”

designing babies is to treat them as objects, not as human beings, and there are a couple of strong philosophical traditions in ethics that go squarely against that (I’m thinking, obviously, of Kant’s categorical imperative, as well as of virtue ethics; not sure what a consequentialist would say about this, probably she would remain neutral on the issue).

Commodification of human beings has historically produced all sorts of bad stuff, from slavery to exploitative prostitution, and arguably to war (after all, we are using our soldiers as means to gain access to power, resources, territory, etc.)

And of course, there is the issue of access. Across-the-board “procreative liberty” of the type envisioned by Savulescu will cost money because it requires considerable resources.

imagine that these parents decide to purchase the ability to produce babies that have the type of characteristics that will make them more successful in society: taller, more handsome, blue eyed, blonde, more symmetrical, whatever. We have just created yet another way for the privileged to augment and pass their privileges to the next generation — in this case literally through their genes, not just as real estate or bank accounts. That would quickly lead to an even further divide between the haves and the have-nots, more inequality, more injustice, possibly, in the long run, even two different species (why not design your babies so that they can’t breed with certain types of undesirables, for instance?). Is that the sort of society that Savulescu is willing to envision in the name of his total procreative liberty? That begins to sounds like the libertarian version of the eugenic ideal, something potentially only slightly less nightmarish than the early 20th century original.

Not to mention that rich people can provide better health care, education and nutrition to their children and again no one is questioning their right to do so. Wouldn't a couple of inches be pretty negligible compared to getting into a good school? Aren't we applying double standards by objecting to this issue alone? Do we really live in a society that values equal opportunities? People (may) be equal before the law but they are not equal to each other and each one of us is tacitly accepting that fact when we acknowledge the social hierarchy (in other words, every time we interact with someone who is our superior). I am not crazy about this fact but that's just how people are and this has to be taken into account when discussing this.

More critical, in my opinion, is the implication that skeptical research and communication happens in an ethical vacuum. That just isn’t true. Indeed, it is dangerous for a field which promotes and attacks medical treatments, accuses people of crimes, opines about law enforcement practices, offers consumer advice, and undertakes educational projects to pretend that it is free from ethical implications — or obligations.

there is no monolithic “one true way to do skepticism.” No, the skeptical world does not break down to nice skeptics who get everything right, and mean skeptics who get everything wrong. (I’m reminded of a quote: “If only there were evil people somewhere insidiously committing evil deeds, and it were necessary only to separate them from the rest of us and destroy them. But the line dividing good and evil cuts through the heart of every human being.”) No one has all the answers. Certainly I don’t, and neither does Phil Plait. Nor has anyone actually proposed a uniform, lockstep approach to skepticism. (No one has any ability to enforce such a thing, in any event.)

However, none of that implies that all approaches to skepticism are equally valid, useful, or good. As in other fields, various skeptical practices do more or less good, cause greater or lesser harm, or generate various combinations of both at the same time. For that reason, skeptics should strive to find ways to talk seriously about the practices and the ethics of our field. Skepticism has blossomed into something that touches a lot of lives — and yet it is an emerging field, only starting to come into its potential. We need to be able to talk about that potential, and about the pitfalls too.

All of the fields from which skepticism borrows (such as medicine, education, psychology, journalism, history, and even arts like stage magic and graphic design) have their own standards of professional ethics. In some cases those ethics are well-explored professional fields in their own right (consider medical ethics, a field with its own academic journals and doctoral programs). In other cases those ethical guidelines are contested, informal, vague, or honored more in the breach. But in every case, there are serious conversations about the ethical implications of professional practice, because those practices impact people’s lives. Why would skepticism be any different?

, Skeptrack speaker Barbara Drescher (a cognitive pyschologist who teaches research methodology) described the complexity of research ethics in her own field. Imagine, she said, that a psychologist were to ask research subjects a question like, “Do your parents like the color red?” Asking this may seem trivial and harmless, but it is nonetheless an ethical trade-off with associated risks (however small) that psychological researchers are ethically obliged to confront. What harm might that question cause if a research subject suffers from erythrophobia, or has a sick parent — or saw their parents stabbed to death?

When skeptics undertake scientific, historical, or journalistic research, we should (I argue) consider ourselves bound by some sort of research ethics. For now, we’ll ignore the deeper, detailed question of what exactly that looks like in practical terms (when can skeptics go undercover or lie to get information? how much research does due diligence require? and so on). I’d ask only that we agree on the principle that skeptical research is not an ethical free-for-all.

when skeptics communicate with the public, we take on further ethical responsibilities — as do doctors, journalists, and teachers. We all accept that doctors are obliged to follow some sort of ethical code, not only of due diligence and standard of care, but also in their confidentiality, manner, and the factual information they disclose to patients. A sentence that communicates a diagnosis, prescription, or piece of medical advice (“you have cancer” or “undertake this treatment”) is not a contextless statement, but a weighty, risky, ethically serious undertaking that affects people’s lives. It matters what doctors say, and it matters how they say it.

Grassroots Ethics It happens that skepticism is my professional field. It’s natural that I should feel bound by the central concerns of that field. How can we gain reliable knowledge about weird things? How can we communicate that knowledge effectively? And, how can we pursue that practice ethically?

At the same time, most active skeptics are not professionals. To what extent should grassroots skeptics feel obligated to consider the ethics of skeptical activism? Consider my own status as a medical amateur. I almost need super-caps-lock to explain how much I am not a doctor. My medical training began and ended with a couple First Aid courses (and those way back in the day). But during those short courses, the instructors drummed into us the ethical considerations of our minimal training. When are we obligated to perform first aid? When are we ethically barred from giving aid? What if the injured party is unconscious or delirious? What if we accidentally kill or injure someone in our effort to give aid? Should we risk exposure to blood-borne illnesses? And so on. In a medical context, ethics are determined less by professional status, and more by the harm we can cause or prevent by our actions.

police officers are barred from perjury, and journalists from libel — and so are the lay public. We expect schoolteachers not to discuss age-inappropriate topics with our young children, or to persuade our children to adopt their religion; when we babysit for a neighbor, we consider ourselves bound by similar rules. I would argue that grassroots skeptics take on an ethical burden as soon as they speak out on medical matters, legal matters, or other matters of fact, whether from platforms as large as network television, or as small as a dinner party. The size of that burden must depend somewhat on the scale of the risks: the number of people reached, the certainty expressed, the topics tackled.

tu-quoque argument.

How much time are skeptics going to waste, arguing in a circular firing squad about each other’s free speech? Like it or not, there will always be confrontational people. You aren’t going to get a group of people as varied as skeptics are, and make them all agree to “be nice”. It’s a pipe dream, and a waste of time.