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Weiye Loh

Jonathan Stray » Measuring and improving accuracy in journalism - 0 views

jonathanstray.com/reasing-accuracy-in-journalism

Journalism Accuracy Objectivity Methodology Research

shared by Weiye Loh on 12 May 11 - No Cached
  • Accuracy is a hard thing to measure because it’s a hard thing to define. There are subjective and objective errors, and no standard way of determining whether a reported fact is true or false
  • The last big study of mainstream reporting accuracy found errors (defined below) in 59% of 4,800 stories across 14 metro newspapers. This level of inaccuracy — where about one in every two articles contains an error — has persisted for as long as news accuracy has been studied, over seven decades now.
  • With the explosion of available information, more than ever it’s time to get serious about accuracy, about knowing which sources can be trusted. Fortunately, there are emerging techniques that might help us to measure media accuracy cheaply, and then increase it.
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  • We could continuously sample a news source’s output to produce ongoing accuracy estimates, and build social software to help the audience report and filter errors. Meticulously applied, this approach would give a measure of the accuracy of each information source, and a measure of the efficiency of their corrections process (currently only about 3% of all errors are corrected.)
  • Real world reporting isn’t always clearly “right” or “wrong,” so it will often be hard to decide whether something is an error or not. But we’re not going for ultimate Truth here,  just a general way of measuring some important aspect of the idea we call “accuracy.” In practice it’s important that the error counting method is simple, clear and repeatable, so that you can compare error rates of different times and sources.
  • Subjective errors, though by definition involving judgment, should not be dismissed as merely differences in opinion. Sources found such errors to be about as common as factual errors and often more egregious [as rated by the sources.] But subjective errors are a very complex category
  • One of the major problems with previous news accuracy metrics is the effort and time required to produce them. In short, existing accuracy measurement methods are expensive and slow. I’ve been wondering if we can do better, and a simple idea comes to mind: sampling. The core idea is this: news sources could take an ongoing random sample of their output and check it for accuracy — a fact check spot check
  • Standard statistical theory tells us what the error on that estimate will be for any given number of samples (If I’ve got this right, the relevant formula is standard error of a population proportion estimate without replacement.) At a sample rate of a few stories per day, daily estimates of error rate won’t be worth much. But weekly and monthly aggregates will start to produce useful accuracy estimates
  • the first step would be admitting how inaccurate journalism has historically been. Then we have to come up with standardized accuracy evaluation procedures, in pursuit of metrics that capture enough of what we mean by “true” to be worth optimizing. Meanwhile, we can ramp up the efficiency of our online corrections processes until we find as many useful, legitimate errors as possible with as little staff time as possible. It might also be possible do data mining on types of errors and types of stories to figure out if there are patterns in how an organization fails to get facts right.
  • I’d love to live in a world where I could compare the accuracy of information sources, where errors got found and fixed with crowd-sourced ease, and where news organizations weren’t shy about telling me what they did and did not know. Basic factual accuracy is far from the only measure of good journalism, but perhaps it’s an improvement over the current sad state of affairs
  • Weiye Loh on 12 May 11
     
    Professional journalism is supposed to be "factual," "accurate," or just plain true. Is it? Has news accuracy been getting better or worse in the last decade? How does it vary between news organizations, and how do other information sources rate? Is professional journalism more or less accurate than everything else on the internet? These all seem like important questions, so I've been poking around, trying to figure out what we know and don't know about the accuracy of our news sources. Meanwhile, the online news corrections process continues to evolve, which gives us hope that the news will become more accurate in the future.
Weiye Loh

Straits Times Forum explains why it heavily edited letter | The Online Citizen - 0 views

theonlinecitizen.com/...s-why-it-heavily-edited-letter

News media bias Statistics Representation Singapore

shared by Weiye Loh on 28 Mar 11 - No Cached
  • 1. You stated we wrongly replaced the statistic you cited with another from Ms Rachel Chang’s article on March 8 (“School system still the ‘best way to move up’). Your original letter “It is indeed heartwarming to learn that 90% of children from one-to-three-room flats do not make it to university.” Reasons we edited it: Factual error, sense. There were two problems with your sentence. First, it was contradictory and didn’t make sense.Your original sentence cannot mean what it says unless you were elated over the fact that nine in 10 children from less well-off homes failed to qualify for university. So we edited it for sense, i.e., underscoring a positive feeling (heartwarming) with a positive fact; rather than the self-penned irony of a positive feeling (heartwarming) backed by a negative fact (90% failure rate to university admission by less well off children). That was why we replaced the original statistic with the only one in Ms Chang’s March 8 report that matched your elation, that is, that 50 percent of less well off children found tertiary success.
  • (Visa: Firstly, I find it hard to believe that nobody in the Straits Times office understands the meaning of sarcasm. Secondly, there was NO FACTUAL ERROR. Allow me to present to you the statistics, direct from The Straits Times themselves: http://www.straitstimes.com/STI/STIMEDIA/pdf/20110308/a10.pdf )
  • Second, we replaced your original statistic because it did not exist in Ms Chang’s March 8 front-page report. Ms Chang quoted that statistic in a later article (“Poor kids need aspiration: March 18; paragraph 5), which appeared after your letter was published. (Visa: It did not exist? Pay careful attention to the URL: http://www.straitstimes.com/STI/STIMEDIA/pdf/20110308/a10.pdf . Look at the number. 20110308. 2011 03 08. 8th March 2011.)
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  • 2. Your original letter “His (Education Minister Dr Ng) statement is backed up with the statistic that 50% of children from the bottom third of the socio-economic ladder score in the bottom third of the Primary School Leaving Examination. “ Reason we edited it: Factual error
  • “His statement is backed by the statistic that about 50 per cent of children from the bottom third of the socio-economic bracket score within the top two-thirds of their Primary School Leaving Examination cohort. (Para 3 of Ms Chang’s March 8 report). (Visa:  THIS IS NOT A FACTUAL ERROR. If 50% of a group score in the top two-thirds, then the remaining 50% of the group, by simple process of elimination, must score in the bottom third!)
  • You can assume that the stats are wrong, but you CANNOT CHANGE it and CONTINUE to use the contributor’s name! Where is your journalist moral, ethic, and basic human decency? Since it is YOUR meaning, and not the writer’s, don’t it mean that you ABUSE, FABRICATE, and LIE to the public that that was by Samuel?
  • Either you print a news column or delete the letter. At least have some basic courtesy to call and ASK the writer for changes. Even a kid knows that its basic human decency to ask. HOW come you, as a grown man, YAP KOON HONG, can’t?
  • “So we edited it for sense ……. That was why we replaced the original statistic with the only one in Ms Chang’s March 8 report that matched your elation ……” and “So, we needed to provide the context to the minister’s statement in order to retain the sense of your meaning.” These are extraordinary statements. My understanding is that editors edit for clarity and brevity. It is extraordinary and perhaps only in Singapore that editors also edit for “sense”.
  • 50% make it to university therefore the other 50% did not make it. This kind of reasoning only works in primary or secondary school maths. In the real world, academia and journalism, the above would be considered a logical fallacy. To explain why, one must consider the fact that not going to university is not the same as “not making it”. World class musicians, sports, volunteer work, oversease universities, travel, these are just a few of the reasons why we can’t just do a simple calculation when it comes to statistics. Bill Gates didn’t go to university, would we classify him as “not making it” Sarcasm has no place in journalism as it relies on visual and vocal indicators to interpret. I live in Washington, and if the above letter was sent to any newspaper it would be thrown out with all the other garbage faster than you could say freedom of speech. At least the editor in question here bothered to try his best to get the letter published.
  • “we felt your opinion deserved publication” Please, Yap Koon Hong, what you published was the very opposite of his opinion! As you yourself admitted, Samuel’s letter was ironic in nature, but you removed all traces of irony and changed the statistics to fabricate a sense of “elation” that Samuel did not mean to convey!
Weiye Loh

Odds Are, It's Wrong - Science News - 0 views

www.sciencenews.org/...Odds_Are%2C_Its_Wrong

Statistics Science

shared by Weiye Loh on 20 Mar 10 - Cached
  • science has long been married to mathematics. Generally it has been for the better. Especially since the days of Galileo and Newton, math has nurtured science. Rigorous mathematical methods have secured science’s fidelity to fact and conferred a timeless reliability to its findings.
  • a mutant form of math has deflected science’s heart from the modes of calculation that had long served so faithfully. Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.
  • science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.
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  • 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)
    • Weiye Loh
      Weiye Loh on 20 Mar 10
       
      Does the problem then, lie not in statistics, but the interpretation of statistics? Is the fallacy of appeal to probability is at work in such interpretation? 
  • 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.”
  • Weiye Loh on 20 Mar 10
     
    Odds Are, It's Wrong Science fails to face the shortcomings of statistics
Weiye Loh

Can a group of scientists in California end the war on climate change? | Science | The ... - 0 views

www.guardian.co.uk/...entists-end-climate-change-war

Climate Science Academic Research Data Mining Data Open Source Funding ClimateGate Polarisation Statistics

shared by Weiye Loh on 01 Mar 11 - No Cached
  • Muller calls his latest obsession the Berkeley Earth project. The aim is so simple that the complexity and magnitude of the undertaking is easy to miss. Starting from scratch, with new computer tools and more data than has ever been used, they will arrive at an independent assessment of global warming. The team will also make every piece of data it uses – 1.6bn data points – freely available on a website. It will post its workings alongside, including full information on how more than 100 years of data from thousands of instruments around the world are stitched together to give a historic record of the planet's temperature.
  • Muller is fed up with the politicised row that all too often engulfs climate science. By laying all its data and workings out in the open, where they can be checked and challenged by anyone, the Berkeley team hopes to achieve something remarkable: a broader consensus on global warming. In no other field would Muller's dream seem so ambitious, or perhaps, so naive.
  • "We are bringing the spirit of science back to a subject that has become too argumentative and too contentious," Muller says, over a cup of tea. "We are an independent, non-political, non-partisan group. We will gather the data, do the analysis, present the results and make all of it available. There will be no spin, whatever we find." Why does Muller feel compelled to shake up the world of climate change? "We are doing this because it is the most important project in the world today. Nothing else comes close," he says.
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  • There are already three heavyweight groups that could be considered the official keepers of the world's climate data. Each publishes its own figures that feed into the UN's Intergovernmental Panel on Climate Change. Nasa's Goddard Institute for Space Studies in New York City produces a rolling estimate of the world's warming. A separate assessment comes from another US agency, the National Oceanic and Atmospheric Administration (Noaa). The third group is based in the UK and led by the Met Office. They all take readings from instruments around the world to come up with a rolling record of the Earth's mean surface temperature. The numbers differ because each group uses its own dataset and does its own analysis, but they show a similar trend. Since pre-industrial times, all point to a warming of around 0.75C.
  • You might think three groups was enough, but Muller rolls out a list of shortcomings, some real, some perceived, that he suspects might undermine public confidence in global warming records. For a start, he says, warming trends are not based on all the available temperature records. The data that is used is filtered and might not be as representative as it could be. He also cites a poor history of transparency in climate science, though others argue many climate records and the tools to analyse them have been public for years.
  • Then there is the fiasco of 2009 that saw roughly 1,000 emails from a server at the University of East Anglia's Climatic Research Unit (CRU) find their way on to the internet. The fuss over the messages, inevitably dubbed Climategate, gave Muller's nascent project added impetus. Climate sceptics had already attacked James Hansen, head of the Nasa group, for making political statements on climate change while maintaining his role as an objective scientist. The Climategate emails fuelled their protests. "With CRU's credibility undergoing a severe test, it was all the more important to have a new team jump in, do the analysis fresh and address all of the legitimate issues raised by sceptics," says Muller.
  • This latest point is where Muller faces his most delicate challenge. To concede that climate sceptics raise fair criticisms means acknowledging that scientists and government agencies have got things wrong, or at least could do better. But the debate around global warming is so highly charged that open discussion, which science requires, can be difficult to hold in public. At worst, criticising poor climate science can be taken as an attack on science itself, a knee-jerk reaction that has unhealthy consequences. "Scientists will jump to the defence of alarmists because they don't recognise that the alarmists are exaggerating," Muller says.
  • The Berkeley Earth project came together more than a year ago, when Muller rang David Brillinger, a statistics professor at Berkeley and the man Nasa called when it wanted someone to check its risk estimates of space debris smashing into the International Space Station. He wanted Brillinger to oversee every stage of the project. Brillinger accepted straight away. Since the first meeting he has advised the scientists on how best to analyse their data and what pitfalls to avoid. "You can think of statisticians as the keepers of the scientific method, " Brillinger told me. "Can scientists and doctors reasonably draw the conclusions they are setting down? That's what we're here for."
  • For the rest of the team, Muller says he picked scientists known for original thinking. One is Saul Perlmutter, the Berkeley physicist who found evidence that the universe is expanding at an ever faster rate, courtesy of mysterious "dark energy" that pushes against gravity. Another is Art Rosenfeld, the last student of the legendary Manhattan Project physicist Enrico Fermi, and something of a legend himself in energy research. Then there is Robert Jacobsen, a Berkeley physicist who is an expert on giant datasets; and Judith Curry, a climatologist at Georgia Institute of Technology, who has raised concerns over tribalism and hubris in climate science.
  • Robert Rohde, a young physicist who left Berkeley with a PhD last year, does most of the hard work. He has written software that trawls public databases, themselves the product of years of painstaking work, for global temperature records. These are compiled, de-duplicated and merged into one huge historical temperature record. The data, by all accounts, are a mess. There are 16 separate datasets in 14 different formats and they overlap, but not completely. Muller likens Rohde's achievement to Hercules's enormous task of cleaning the Augean stables.
  • The wealth of data Rohde has collected so far – and some dates back to the 1700s – makes for what Muller believes is the most complete historical record of land temperatures ever compiled. It will, of itself, Muller claims, be a priceless resource for anyone who wishes to study climate change. So far, Rohde has gathered records from 39,340 individual stations worldwide.
  • Publishing an extensive set of temperature records is the first goal of Muller's project. The second is to turn this vast haul of data into an assessment on global warming.
  • The big three groups – Nasa, Noaa and the Met Office – work out global warming trends by placing an imaginary grid over the planet and averaging temperatures records in each square. So for a given month, all the records in England and Wales might be averaged out to give one number. Muller's team will take temperature records from individual stations and weight them according to how reliable they are.
  • This is where the Berkeley group faces its toughest task by far and it will be judged on how well it deals with it. There are errors running through global warming data that arise from the simple fact that the global network of temperature stations was never designed or maintained to monitor climate change. The network grew in a piecemeal fashion, starting with temperature stations installed here and there, usually to record local weather.
  • Among the trickiest errors to deal with are so-called systematic biases, which skew temperature measurements in fiendishly complex ways. Stations get moved around, replaced with newer models, or swapped for instruments that record in celsius instead of fahrenheit. The times measurements are taken varies, from say 6am to 9pm. The accuracy of individual stations drift over time and even changes in the surroundings, such as growing trees, can shield a station more from wind and sun one year to the next. Each of these interferes with a station's temperature measurements, perhaps making it read too cold, or too hot. And these errors combine and build up.
  • This is the real mess that will take a Herculean effort to clean up. The Berkeley Earth team is using algorithms that automatically correct for some of the errors, a strategy Muller favours because it doesn't rely on human interference. When the team publishes its results, this is where the scrutiny will be most intense.
  • Despite the scale of the task, and the fact that world-class scientific organisations have been wrestling with it for decades, Muller is convinced his approach will lead to a better assessment of how much the world is warming. "I've told the team I don't know if global warming is more or less than we hear, but I do believe we can get a more precise number, and we can do it in a way that will cool the arguments over climate change, if nothing else," says Muller. "Science has its weaknesses and it doesn't have a stranglehold on the truth, but it has a way of approaching technical issues that is a closer approximation of truth than any other method we have."
  • It might not be a good sign that one prominent climate sceptic contacted by the Guardian, Canadian economist Ross McKitrick, had never heard of the project. Another, Stephen McIntyre, whom Muller has defended on some issues, hasn't followed the project either, but said "anything that [Muller] does will be well done". Phil Jones at the University of East Anglia was unclear on the details of the Berkeley project and didn't comment.
  • Elsewhere, Muller has qualified support from some of the biggest names in the business. At Nasa, Hansen welcomed the project, but warned against over-emphasising what he expects to be the minor differences between Berkeley's global warming assessment and those from the other groups. "We have enough trouble communicating with the public already," Hansen says. At the Met Office, Peter Stott, head of climate monitoring and attribution, was in favour of the project if it was open and peer-reviewed.
  • Peter Thorne, who left the Met Office's Hadley Centre last year to join the Co-operative Institute for Climate and Satellites in North Carolina, is enthusiastic about the Berkeley project but raises an eyebrow at some of Muller's claims. The Berkeley group will not be the first to put its data and tools online, he says. Teams at Nasa and Noaa have been doing this for many years. And while Muller may have more data, they add little real value, Thorne says. Most are records from stations installed from the 1950s onwards, and then only in a few regions, such as North America. "Do you really need 20 stations in one region to get a monthly temperature figure? The answer is no. Supersaturating your coverage doesn't give you much more bang for your buck," he says. They will, however, help researchers spot short-term regional variations in climate change, something that is likely to be valuable as climate change takes hold.
  • Despite his reservations, Thorne says climate science stands to benefit from Muller's project. "We need groups like Berkeley stepping up to the plate and taking this challenge on, because it's the only way we're going to move forwards. I wish there were 10 other groups doing this," he says.
  • Muller's project is organised under the auspices of Novim, a Santa Barbara-based non-profit organisation that uses science to find answers to the most pressing issues facing society and to publish them "without advocacy or agenda". Funding has come from a variety of places, including the Fund for Innovative Climate and Energy Research (funded by Bill Gates), and the Department of Energy's Lawrence Berkeley Lab. One donor has had some climate bloggers up in arms: the man behind the Charles G Koch Charitable Foundation owns, with his brother David, Koch Industries, a company Greenpeace called a "kingpin of climate science denial". On this point, Muller says the project has taken money from right and left alike.
  • No one who spoke to the Guardian about the Berkeley Earth project believed it would shake the faith of the minority who have set their minds against global warming. "As new kids on the block, I think they will be given a favourable view by people, but I don't think it will fundamentally change people's minds," says Thorne. Brillinger has reservations too. "There are people you are never going to change. They have their beliefs and they're not going to back away from them."
Weiye Loh

Roger Pielke Jr.'s Blog: ClimateWire Correction Request - 0 views

rogerpielkejr.blogspot.com/...tewire-correction-request.html

Science Communication Journalism

shared by Weiye Loh on 08 Mar 11 - No Cached
  • Dear Debra- Your article today contains several major errors in its reporting of the WSJ conference last week. 1. I did not say that the IPCC Himalayan glacier error was "egregious".  I used that term to refer to the IPCC inclusion of a graph on disaster costs and climate change. 2. I did not say or imply (nor do I believe) that the glacier error or UEA emails "cast a shadow on the entire body of research showing evidence of anthropogenic climate change." I did say that the institutions of climate science were poorly prepared for dealing with the allegations of error. 3. Chris Field and I are not "frequent sparring partners."  We have discussed climate issues together publicly only once before. I spent the bulk of the time on the panel discussing the IPCC's treatment of the science of disasters and climate change and the institutional maturity of the climate science community.  I find it remarkable that you ignored those issues. That said, I am requesting that you correct the two serious misquotations of my remarks and the mischaracterization of my relationship with Chris Field. If you choose to contest this I am sure that the WSJ tape from the event can set the record straight. Many thanks, Roger
  • Weiye Loh on 08 Mar 11
     
    I talk to people in the media a lot, and occasionally I am quoted, almost always correctly.  ClimateWire has a story today from a reporter who I did not talk to and whose reporting is not so good. 
Weiye Loh

The Data-Driven Life - NYTimes.com - 0 views

www.nytimes.com/...02self-measurement-t.html

Data

shared by Weiye Loh on 16 Nov 10 - Cached
  • Humans make errors. We make errors of fact and errors of judgment. We have blind spots in our field of vision and gaps in our stream of attention.
  • These weaknesses put us at a disadvantage. We make decisions with partial information. We are forced to steer by guesswork. We go with our gut.
  • Others use data.
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  • Others use data. A timer running on Robin Barooah’s computer tells him that he has been living in the United States for 8 years, 2 months and 10 days. At various times in his life, Barooah — a 38-year-old self-employed software designer from England who now lives in Oakland, Calif. — has also made careful records of his work, his sleep and his diet.
  • A few months ago, Barooah began to wean himself from coffee. His method was precise. He made a large cup of coffee and removed 20 milliliters weekly. This went on for more than four months, until barely a sip remained in the cup. He drank it and called himself cured. Unlike his previous attempts to quit, this time there were no headaches, no extreme cravings. Still, he was tempted, and on Oct. 12 last year, while distracted at his desk, he told himself that he could probably concentrate better if he had a cup. Coffee may have been bad for his health, he thought, but perhaps it was good for his concentration. Barooah wasn’t about to try to answer a question like this with guesswork. He had a good data set that showed how many minutes he spent each day in focused work. With this, he could do an objective analysis. Barooah made a chart with dates on the bottom and his work time along the side. Running down the middle was a big black line labeled “Stopped drinking coffee.” On the left side of the line, low spikes and narrow columns. On the right side, high spikes and thick columns. The data had delivered their verdict, and coffee lost.
  • “People have such very poor sense of time,” Barooah says, and without good time calibration, it is much harder to see the consequences of your actions. If you want to replace the vagaries of intuition with something more reliable, you first need to gather data. Once you know the facts, you can live by them.
Weiye Loh

Major reform for climate body : Nature News - 0 views

www.nature.com/...473261a.html

Climate Science IPCC Error Conflict Bias Politics

shared by Weiye Loh on 06 Jun 11 - No Cached
  • The first major test of these changes will be towards the end of this year, with the release of a report assessing whether climate change is increasing the likelihood of extreme weather events. Despite much speculation, there is scant scientific evidence for such a link — particularly between climate warming, storm frequency and economic losses — and the report is expected to spark renewed controversy. "It'll be interesting to see how the IPCC will handle this hot potato where stakes are high but solid peer-reviewed results are few," says Silke Beck, a policy expert at the Helmholtz Centre for Environmental Research in Leipzig, Germany.
  • Weiye Loh on 06 Jun 11
     
    A new conflict-of-interest policy will require all IPCC officials and authors to disclose financial and other interests relevant to their work (Pachauri had been harshly criticized in 2009 for alleged conflicts of interest.) The meeting also adopted a detailed protocol for addressing errors in existing and future IPCC reports, along with guidelines to ensure that descriptions of scientific uncertainties remain consistent across reports. "This is a heartening and encouraging outcome of the review we started one year ago," Pachauri told Nature. "It will strengthen the IPCC and help restore public trust in the climate sciences."
Weiye Loh

DenialDepot: A word of caution to the BEST project team - 0 views

denialdepot.blogspot.com/...tion-to-best-project-team.html

Berkeley Earth Climate Science Denial

shared by Weiye Loh on 13 Apr 11 - No Cached
  • 1) Any errors, however inconsequential, will be taken Very Seriously and accusations of fraud will be made.
  • 2) If you adjust the raw data we will accuse you of fraudulently fiddling the figures whilst cooking the books.3) If you don't adjust the raw data we will accuse you of fraudulently failing to account for station biases and UHI.
  • 7) By all means publish all your source code, but we will still accuse you of hiding the methodology for your adjustments.
  • ...10 more annotations...
  • 8) If you publish results to your website and errors are found, we will accuse you of a Very Serious Error irregardless of severity (see point #1) and bemoan the press release you made about your results even though you won't remember making any press release about your results.
  • 9) With regard to point #8 above, at extra cost and time to yourself you must employ someone to thoroughly check each monthly update before is is published online, even if this delays publication of the results till the end of the month. You might be surprised at this because no-one actually relies on such freshly published data anyway and aren't the many eyes of blog audit better than a single pair of eyes? Well that's irrelevant. See points #1 and #810) If you don't publish results promptly at the start of the month on the public website, but instead say publish the results to a private site for checks to be performed before release, we will accuse you of engaging in unscientific-like secrecy and massaging the data behind closed doors.
  • 14) If any region/station shows a warming trend that doesn't match the raw data, and we can't understand why, we will accuse you of fraud and dismiss the entire record. Don't expect us to have to read anything to understand results.
  • 15) You must provide all input datasets on your website. It's no good referencing NOAAs site and saying they "own" the GHCN data for example. I don't want their GHCN raw temperatures file, I want the one on your hard drive which you used for the analysis, even if you claim they are the same. If you don't do this we will accuse you of hiding the data and preventing us checking your results.
  • 24. In the event that you comply with all of the above, we will point out that a mere hundred-odd years of data is irrelevant next to the 4.5 billion year history of Earth. So why do you even bother?
  • 23) In the unlikely event that I haven't wasted enough of your time forcing you to comply with the above rules, I also demand to see all emails you have sent or will send during the period 1950 to 2050 that contain any of these keywords
  • 22) We don't need any scrutiny because our role isn't important.
  • 17) We will treat your record as if no alternative exists. As if your record is the make or break of Something Really Important (see point #1) and we just can't check the results in any other way.
  • 16) You are to blame for any station data your team uses. If we find out that a station you use is next to an AC Unit, we will conclude you personally planted the thermometer there to deliberately get warming.
  • an article today by Roger Pielke Nr. (no relation) that posited the fascinating concept that thermometers are just as capricious and unreliable proxies for temperature as tree rings. In fact probably more so, and re-computing global temperature by gristlecone pines would reveal the true trend of global cooling, which will be in all our best interests and definitely NOT just those of well paying corporate entities.
  • Weiye Loh on 13 Apr 11
     
    Dear Professor Muller and Team, If you want your Berkley Earth Surface Temperature project to succeed and become the center of attention you need to learn from the vast number of mistakes Hansen and Jones have made with their temperature records. To aid this task I created a point by point list for you.
Weiye Loh

Balderdash - 0 views

gssq.blogspot.com/...y-is-there-no-god-but-try.html

shared by Weiye Loh on 10 Sep 09 - Cached
  • A letter Paul wrote to complain about the "The Dead Sea Scrolls" exhibition at the Arts House:To Ms. Amira Osman (Marketing and Communications Manager),cc.Colin Goh, General Manager,Florence Lee, Depury General ManagerDear Ms. Osman,I visited the Dead Sea Scrolls “exhibition” today with my wife. Thinking that it was from a legitimate scholarly institute or (how naïve of me!) the Israel Antiquities Authority, I was looking forward to a day of education and entertainment.Yet when I got it, much of the exhibition (and booklets) merely espouses an evangelical (fundamentalist) view of the Bible – there are booklets on the inerrancy of the Bible, on how archaeology has proven the Bible to be true etc.Apart from these there are many blatant misrepresentations of the state of archaeology and mainstream biblical scholarship:a) There was initial screening upon entry of a 5-10 minute pseudo-documentary on the Dead Sea Scrolls. A presenter (can’t remember the name) was described as a “biblical archaeologist” – a term that no serious archaeologist working in the Levant would apply to him or herself. (Some prefer the term “Syro-Palestinian archaeologist” but almost all reject the term “biblical archaeologist”). See the book by Thomas W. Davis, “Shifting Sands: The Rise and Fall of Biblical Archaeology”, Oxford, New York 2004. Davis is an actual archaeologist working in the field and the book tells why the term “Biblical archaeologist” is not considered a legitimate term by serious archaeologist.b) In the same presentation, the presenter made the erroneous statement that the entire old testament was translated into Greek in the third century BCE. This is a mistake – only the Pentateuch (the first five books of the Old Testament) was translated during that time. Note that this ‘error’ is not inadvertent but is a familiar claim by evangelical apologists who try to argue for an early date of all the books of the Old testament - if all the books have been translated by the third century BCE obviously these books must all have been written before then! This flies against modern scholarship which show that some books in the Old Testament such as the Book of Daniel was written only in the second century BCE]The actual state of scholarship on the Septuagint [The Greek translation of the Bible] is accurately given in the book by Ernst Würthwein, “The Text of the Old Testament” – Eerdmans 1988 pp.52-54c) Perhaps the most blatant error was one which claimed that the “Magdalene fragments” – which contains the 26th chapter of the Gospel of Matthew is dated to 50 AD!!! Scholars are unanimous in dating these fragments to 200 AD. The only ‘scholar’ cited that dated these fragments to 50 AD was the German papyrologist Carsten Thiede – a well know fundamentalist. This is what Burton Mack (a critical – legitimate – NT scholar) has to say about Thiede’s eccentric dating “From a critical scholar's point of view, Thiede's proposal is an example of just how desperate the Christian imagination can become in the quest to argue for the literal facticity of the Christian gospels” [Mack, Burton L., “Who Wrote the New Testament?:The Making of the Christian Myth” HarperCollins, San Francisco 1995] Yet the dating of 50 AD is presented as though it is a scholarly consensus position!In fact the last point was so blatant that I confronted the exhibitors. (Tak Boleh Tahan!!) One American exhibitor told me that “Yes, it could have been worded differently, but then we would have to change the whole display” (!!). When I told him that this was not a typo but a blatant attempt to deceive, he mentioned that Theide’s views are supported by “The Dallas Theological Seminary” – another well know evangelical institute!I have no issue with the religious strengthening their faith by having their own internal exhibitions on historical artifacts etc. But when it is presented to the public as a scholarly exhibition – this is quite close to being dishonest.I felt cheated of the $36 dollars I paid for the tickets and of the hour that I spent there before realizing what type of exhibition it was.I am disappointed with The Art House for show casing this without warning potential visitors of its clear religious bias.Yours sincerely,Paul TobinTo their credit, the Arts House speedily replied.
    • Weiye Loh
      Weiye Loh on 10 Sep 09
       
      The issue of truth is indeed so maddening. Certainly, the 'production' of truth has been widely researched and debated by scholars. Spivak for example, argued for the deconstruction by means of questioning the privilege of identity so that someone is believed to have the truth. And along the same line, albeit somewhat misunderstood I feel, It was mentioned in class that somehow people who are oppressed know better.
Weiye Loh

Libertarianism Is Marxism of the Right - 4 views

http://www.commongroundcommonsense.org/forums/lofiversion/index.php/t21933.html "Because 95 percent of the libertarianism one encounters at cocktail parties, on editorial pages, and on Capitol Hil...

Libertarianism Marxism

started by Weiye Loh on 28 Aug 09 no follow-up yet
Weiye Loh

The Decline Effect and the Scientific Method : The New Yorker - 0 views

www.newyorker.com/...101213fa_fact_lehrer

Truth Science Evidence Data

shared by Weiye Loh on 05 Jan 11 - No Cached
  • On September 18, 2007, a few dozen neuroscientists, psychiatrists, and drug-company executives gathered in a hotel conference room in Brussels to hear some startling news. It had to do with a class of drugs known as atypical or second-generation antipsychotics, which came on the market in the early nineties.
  • the therapeutic power of the drugs appeared to be steadily waning. A recent study showed an effect that was less than half of that documented in the first trials, in the early nineteen-nineties. Many researchers began to argue that the expensive pharmaceuticals weren’t any better than first-generation antipsychotics, which have been in use since the fifties. “In fact, sometimes they now look even worse,” John Davis, a professor of psychiatry at the University of Illinois at Chicago, told me.
  • Before the effectiveness of a drug can be confirmed, it must be tested and tested again. Different scientists in different labs need to repeat the protocols and publish their results. The test of replicability, as it’s known, is the foundation of modern research. Replicability is how the community enforces itself. It’s a safeguard for the creep of subjectivity. Most of the time, scientists know what results they want, and that can influence the results they get. The premise of replicability is that the scientific community can correct for these flaws.
  • ...30 more annotations...
  • But now all sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants: Davis has a forthcoming analysis demonstrating that the efficacy of antidepressants has gone down as much as threefold in recent decades.
  • the effect is especially troubling because of what it exposes about the scientific process. If replication is what separates the rigor of science from the squishiness of pseudoscience, where do we put all these rigorously validated findings that can no longer be proved? Which results should we believe? Francis Bacon, the early-modern philosopher and pioneer of the scientific method, once declared that experiments were essential, because they allowed us to “put nature to the question.” But it appears that nature often gives us different answers.
  • At first, he assumed that he’d made an error in experimental design or a statistical miscalculation. But he couldn’t find anything wrong with his research. He then concluded that his initial batch of research subjects must have been unusually susceptible to verbal overshadowing. (John Davis, similarly, has speculated that part of the drop-off in the effectiveness of antipsychotics can be attributed to using subjects who suffer from milder forms of psychosis which are less likely to show dramatic improvement.) “It wasn’t a very satisfying explanation,” Schooler says. “One of my mentors told me that my real mistake was trying to replicate my work. He told me doing that was just setting myself up for disappointment.”
  • In private, Schooler began referring to the problem as “cosmic habituation,” by analogy to the decrease in response that occurs when individuals habituate to particular stimuli. “Habituation is why you don’t notice the stuff that’s always there,” Schooler says. “It’s an inevitable process of adjustment, a ratcheting down of excitement. I started joking that it was like the cosmos was habituating to my ideas. I took it very personally.”
  • The most likely explanation for the decline is an obvious one: regression to the mean. As the experiment is repeated, that is, an early statistical fluke gets cancelled out. The extrasensory powers of Schooler’s subjects didn’t decline—they were simply an illusion that vanished over time. And yet Schooler has noticed that many of the data sets that end up declining seem statistically solid—that is, they contain enough data that any regression to the mean shouldn’t be dramatic. “These are the results that pass all the tests,” he says. “The odds of them being random are typically quite remote, like one in a million. This means that the decline effect should almost never happen. But it happens all the time!
  • this is why Schooler believes that the decline effect deserves more attention: its ubiquity seems to violate the laws of statistics. “Whenever I start talking about this, scientists get very nervous,” he says. “But I still want to know what happened to my results. Like most scientists, I assumed that it would get easier to document my effect over time. I’d get better at doing the experiments, at zeroing in on the conditions that produce verbal overshadowing. So why did the opposite happen? I’m convinced that we can use the tools of science to figure this out. First, though, we have to admit that we’ve got a problem.”
  • In 2001, Michael Jennions, a biologist at the Australian National University, set out to analyze “temporal trends” across a wide range of subjects in ecology and evolutionary biology. He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance. In fact, even when numerous variables were controlled for—Jennions knew, for instance, that the same author might publish several critical papers, which could distort his analysis—there was still a significant decrease in the validity of the hypothesis, often within a year of publication. Jennions admits that his findings are troubling, but expresses a reluctance to talk about them publicly. “This is a very sensitive issue for scientists,” he says. “You know, we’re supposed to be dealing with hard facts, the stuff that’s supposed to stand the test of time. But when you see these trends you become a little more skeptical of things.”
  • the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data. It was too exciting an idea to disprove, at least back then.
  • the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.
  • Jennions, similarly, argues that the decline effect is largely a product of publication bias, or the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for. A “significant” result is defined as any data point that would be produced by chance less than five per cent of the time. This ubiquitous test was invented in 1922 by the English mathematician Ronald Fisher, who picked five per cent as the boundary line, somewhat arbitrarily, because it made pencil and slide-rule calculations easier. Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments. In recent years, publication bias has mostly been seen as a problem for clinical trials, since pharmaceutical companies are less interested in publishing results that aren’t favorable. But it’s becoming increasingly clear that publication bias also produces major distortions in fields without large corporate incentives, such as psychology and ecology.
  • While publication bias almost certainly plays a role in the decline effect, it remains an incomplete explanation. For one thing, it fails to account for the initial prevalence of positive results among studies that never even get submitted to journals. It also fails to explain the experience of people like Schooler, who have been unable to replicate their initial data despite their best efforts
  • an equally significant issue is the selective reporting of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.
  • The funnel graph visually captures the distortions of selective reporting. For instance, after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results.
  • Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting is everywhere in science, I got quite depressed,” Palmer told me. “As a researcher, you’re always aware that there might be some nonrandom patterns, but I had no idea how widespread it is.” In a recent review article, Palmer summarized the impact of selective reporting on his field: “We cannot escape the troubling conclusion that some—perhaps many—cherished generalities are at best exaggerated in their biological significance and at worst a collective illusion nurtured by strong a-priori beliefs often repeated.”
  • Palmer emphasizes that selective reporting is not the same as scientific fraud. Rather, the problem seems to be one of subtle omissions and unconscious misperceptions, as researchers struggle to make sense of their results. Stephen Jay Gould referred to this as the “shoehorning” process. “A lot of scientific measurement is really hard,” Simmons told me. “If you’re talking about fluctuating asymmetry, then it’s a matter of minuscule differences between the right and left sides of an animal. It’s millimetres of a tail feather. And so maybe a researcher knows that he’s measuring a good male”—an animal that has successfully mated—“and he knows that it’s supposed to be symmetrical. Well, that act of measurement is going to be vulnerable to all sorts of perception biases. That’s not a cynical statement. That’s just the way human beings work.”
  • One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials. Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits. As Palmer notes, this wide discrepancy suggests that scientists find ways to confirm their preferred hypothesis, disregarding what they don’t want to see. Our beliefs are a form of blindness.
  • John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.”
  • In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals. Forty-five of these studies reported positive results, suggesting that the intervention being tested was effective. Because most of these studies were randomized controlled trials—the “gold standard” of medical evidence—they tended to have a significant impact on clinical practice, and led to the spread of treatments such as hormone replacement therapy for menopausal women and daily low-dose aspirin to prevent heart attacks and strokes. Nevertheless, the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.
  • The situation is even worse when a subject is fashionable. In recent years, for instance, there have been hundreds of studies on the various genes that control the differences in disease risk between men and women. These findings have included everything from the mutations responsible for the increased risk of schizophrenia to the genes underlying hypertension. Ioannidis and his colleagues looked at four hundred and thirty-two of these claims. They quickly discovered that the vast majority had serious flaws. But the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”
  • the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher. “The scientists are so eager to pass this magical test that they start playing around with the numbers, trying to find anything that seems worthy,” Ioannidis says. In recent years, Ioannidis has become increasingly blunt about the pervasiveness of the problem. One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”
  • The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies that show a strong effect. They really want to believe that it’s true.”
  • scientists need to become more rigorous about data collection before they publish. “We’re wasting too much time chasing after bad studies and underpowered experiments,” he says. The current “obsession” with replicability distracts from the real problem, which is faulty design. He notes that nobody even tries to replicate most science papers—there are simply too many. (According to Nature, a third of all studies never even get cited, let alone repeated.)
  • Schooler recommends the establishment of an open-source database, in which researchers are required to outline their planned investigations and document all their results. “I think this would provide a huge increase in access to scientific work and give us a much better way to judge the quality of an experiment,” Schooler says. “It would help us finally deal with all these issues that the decline effect is exposing.”
  • Although such reforms would mitigate the dangers of publication bias and selective reporting, they still wouldn’t erase the decline effect. This is largely because scientific research will always be shadowed by a force that can’t be curbed, only contained: sheer randomness. Although little research has been done on the experimental dangers of chance and happenstance, the research that exists isn’t encouraging
  • John Crabbe, a neuroscientist at the Oregon Health and Science University, conducted an experiment that showed how unknowable chance events can skew tests of replicability. He performed a series of experiments on mouse behavior in three different science labs: in Albany, New York; Edmonton, Alberta; and Portland, Oregon. Before he conducted the experiments, he tried to standardize every variable he could think of. The same strains of mice were used in each lab, shipped on the same day from the same supplier. The animals were raised in the same kind of enclosure, with the same brand of sawdust bedding. They had been exposed to the same amount of incandescent light, were living with the same number of littermates, and were fed the exact same type of chow pellets. When the mice were handled, it was with the same kind of surgical glove, and when they were tested it was on the same equipment, at the same time in the morning.
  • The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results. “If any set of experiments should have passed the test, it should have been ours,” Crabbe says. “But that’s not the way it turned out.” In one experiment, Crabbe injected a particular strain of mouse with cocaine. In Portland the mice given the drug moved, on average, six hundred centimetres more than they normally did; in Albany they moved seven hundred and one additional centimetres. But in the Edmonton lab they moved more than five thousand additional centimetres. Similar deviations were observed in a test of anxiety. Furthermore, these inconsistencies didn’t follow any detectable pattern. In Portland one strain of mouse proved most anxious, while in Albany another strain won that distinction.
  • The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise. The hyperactivity of those coked-up Edmonton mice wasn’t an interesting new fact—it was a meaningless outlier, a by-product of invisible variables we don’t understand. The problem, of course, is that such dramatic findings are also the most likely to get published in prestigious journals, since the data are both statistically significant and entirely unexpected. Grants get written, follow-up studies are conducted. The end result is a scientific accident that can take years to unravel.
  • This suggests that the decline effect is actually a decline of illusion.
  • While Karl Popper imagined falsification occurring with a single, definitive experiment—Galileo refuted Aristotelian mechanics in an afternoon—the process turns out to be much messier than that. Many scientific theories continue to be considered true even after failing numerous experimental tests. Verbal overshadowing might exhibit the decline effect, but it remains extensively relied upon within the field. The same holds for any number of phenomena, from the disappearing benefits of second-generation antipsychotics to the weak coupling ratio exhibited by decaying neutrons, which appears to have fallen by more than ten standard deviations between 1969 and 2001. Even the law of gravity hasn’t always been perfect at predicting real-world phenomena. (In one test, physicists measuring gravity by means of deep boreholes in the Nevada desert found a two-and-a-half-per-cent discrepancy between the theoretical predictions and the actual data.) Despite these findings, second-generation antipsychotics are still widely prescribed, and our model of the neutron hasn’t changed. The law of gravity remains the same.
  • Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling. Not because it reveals the human fallibility of science, in which data are tweaked and beliefs shape perceptions. (Such shortcomings aren’t surprising, at least for scientists.) And not because it reveals that many of our most exciting theories are fleeting fads and will soon be rejected. (That idea has been around since Thomas Kuhn.) The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that’s often not the case. Just because an idea is true doesn’t mean it can be proved. And just because an idea can be proved doesn’t mean it’s true. When the experiments are done, we still have to choose what to believe.
Weiye Loh

Himalayan glaciers not melting because of climate change, report finds - Telegraph - 0 views

www.telegraph.co.uk/...imate-change-report-finds.html

Glaciers Climate Science AGW

shared by Weiye Loh on 30 Jan 11 - No Cached
  • Himalayan glaciers are actually advancing rather than retreating, claims the first major study since a controversial UN report said they would be melted within quarter of a century.
  • Researchers have discovered that contrary to popular belief half of the ice flows in the Karakoram range of the mountains are actually growing rather than shrinking.
  • The discovery adds a new twist to the row over whether global warming is causing the world's highest mountain range to lose its ice cover.
  • ...13 more annotations...
  • It further challenges claims made in a 2007 report by the UN's Intergovernmental Panel on Climate Change that the glaciers would be gone by 2035.
  • Although the head of the panel Dr Rajendra Pachauri later admitted the claim was an error gleaned from unchecked research, he maintained that global warming was melting the glaciers at "a rapid rate", threatening floods throughout north India.
  • The new study by scientists at the Universities of California and Potsdam has found that half of the glaciers in the Karakoram range, in the northwestern Himlaya, are in fact advancing and that global warming is not the deciding factor in whether a glacier survives or melts.
  • Dr Bodo Bookhagen, Dirk Scherler and Manfred Strecker studied 286 glaciers between the Hindu Kush on the Afghan-Pakistan border to Bhutan, taking in six areas.Their report, published in the journal Nature Geoscience, found the key factor affecting their advance or retreat is the amount of debris – rocks and mud – strewn on their surface, not the general nature of climate change.
  • Glaciers surrounded by high mountains and covered with more than two centimetres of debris are protected from melting.Debris-covered glaciers are common in the rugged central Himalaya, but they are almost absent in subdued landscapes on the Tibetan Plateau, where retreat rates are higher.
  • In contrast, more than 50 per cent of observed glaciers in the Karakoram region in the northwestern Himalaya are advancing or stable.
  • "Our study shows that there is no uniform response of Himalayan glaciers to climate change and highlights the importance of debris cover for understanding glacier retreat, an effect that has so far been neglected in predictions of future water availability or global sea level," the authors concluded.
  • Dr Bookhagen said their report had shown "there is no stereotypical Himalayan glacier" in contrast to the UN's climate change report which, he said, "lumps all Himalayan glaciers together."
  • Dr Pachauri, head of the Nobel prize-winning UN Intergovernmental Panel on Climate Change, has remained silent on the matter since he was forced to admit his report's claim that the Himalayan glaciers would melt by 2035 was an error and had not been sourced from a peer-reviewed scientific journal. It came from a World Wildlife Fund report.
  • this latest tawdry addition to the pathetic lies of the Reality Deniers. If you go to a proper source which quotes the full study such as:http://www.sciencedaily.com/re...you discover that the findings of this study are rather different to those portrayed here.
  • only way to consistently maintain a lie is to refuse point-blank to publish ALL the findings of a study, but to cherry-pick the bits which are consistent with the ongoing lie, while ignoring the rest.
  • Bookhagen noted that glaciers in the Karakoram region of Northwestern Himalaya are mostly stagnating. However, glaciers in the Western, Central, and Eastern Himalaya are retreating, with the highest retreat rates -- approximately 8 meters per year -- in the Western Himalayan Mountains. The authors found that half of the studied glaciers in the Karakoram region are stable or advancing, whereas about two-thirds are in retreat elsewhere throughout High Asia
  • glaciers in the steep Himalaya are not only affected by temperature and precipitation, but also by debris coverage, and have no uniform and less predictable response, explained the authors. The debris coverage may be one of the missing links to creating a more coherent picture of glacial behavior throughout all mountains. The scientists contrast this Himalayan glacial study with glaciers from the gently dipping, low-relief Tibetan Plateau that have no debris coverage. Those glaciers behave in a different way, and their frontal changes can be explained by temperature and precipitation changes.
Weiye Loh

How We Know by Freeman Dyson | The New York Review of Books - 0 views

www.nybooks.com/...how-we-know

Information Language Data History Science

shared by Weiye Loh on 27 Feb 11 - No Cached
  • Another example illustrating the central dogma is the French optical telegraph.
  • The telegraph was an optical communication system with stations consisting of large movable pointers mounted on the tops of sixty-foot towers. Each station was manned by an operator who could read a message transmitted by a neighboring station and transmit the same message to the next station in the transmission line.
  • The distance between neighbors was about seven miles. Along the transmission lines, optical messages in France could travel faster than drum messages in Africa. When Napoleon took charge of the French Republic in 1799, he ordered the completion of the optical telegraph system to link all the major cities of France from Calais and Paris to Toulon and onward to Milan. The telegraph became, as Claude Chappe had intended, an important instrument of national power. Napoleon made sure that it was not available to private users.
  • ...27 more annotations...
  • 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.
Weiye Loh

Facial Recognition Software Singles Out Innocent Man | The Utopianist - Think Bigger - 0 views

utopianist.com/...tware-singles-out-innocent-man

Image Facial Recognition Law Technology

shared by Weiye Loh on 19 Jul 11 - No Cached
  • Gass was at home when he got a letter from the Massachusetts Registry of Motor Vehicles saying his license had been revoked. Why? The Boston Globe explains: An antiterrorism computerized facial recognition system that scans a database of millions of state driver’s license images had picked his as a possible fraud. It turned out Gass was flagged because he looks like another driver, not because his image was being used to create a fake identity. His driving privileges were returned but, he alleges in a lawsuit, only after 10 days of bureaucratic wrangling to prove he is who he says he is.
  • Weiye Loh on 19 Jul 11
     
    While a boon to police departments looking to save time and money fighting identity fraud, it's frightening to think that people are having their lives seriously disrupted thanks to computer errors. If you are, say, a truck driver, something like this could cause you weeks of lost pay, something many Americans just can't afford to do. And what if this technology expands beyond just rooting out identity fraud? What if you were slammed against a car hood as police falsely identified you as a criminal? The fact that Hass didn't even have a chance to fight the computer's findings before his license was suspended is especially disturbing. What would you do if this happened to you?
Weiye Loh

flaneurose: The KK Chemo Misdosage Incident - 0 views

flaneurose.blogspot.com/...-chemo-misdosage-incident.html

Medicine Technology Design Capitalism

shared by Weiye Loh on 19 Nov 09 - Cached
  • Labelling the pump that dispenses in ml/hr in a different color from the pump that dispenses in ml/day would be an obvious remedy that would have addressed the KK incident. It's the common-sensical solution that anyone can think of.
  • Sometimes, design flaws like that really do occur because engineers can't see the wood for the trees.
  • But sometimes the team is aware of these issues and highlights them to management, but the manufacturer still proceeds as before. Why is that? Because in addition to design principles, one must be mindful that there are always business considerations at play as well. Manufacturing two (or more) separate designs for pumps incurs greater costs, eliminates the ability to standardize across pumps, increases holding inventory, and overall increases complexity of business and manufacturing processes, and decreases economies of scale. All this naturally reduces profitability.It's not just pumps. Even medicines are typically sold in identical-looking vials with identically colored vial caps, with only the text on the vial labels differentiating them in both drug type and concentration. You can imagine what kinds of accidents can potentially happen there.
  • ...2 more annotations...
  • Legally, the manufacturer has clearly labelled on the pump (in text) the appropriate dosing regime, or for a medicine vial, the type of drug and concentration. The manufacturer has hence fulfilled its duty. Therefore, if there are any mistakes in dosing, the liability for the error lies with the hospital and not the manufacturer of the product. The victim of such a dosing error can be said to be an "externalized cost"; the beneficiaries of the victim's suffering are the manufacturer, who enjoys greater profitability, the hospital, which enjoys greater cost-savings, and the public, who save on healthcare. Is it ethical of the manufacturer, to "pass on" liability to the hospital? To make it difficult (or at least not easy) for the hospital to administer the right dosage? Maybe the manufacturer is at fault, but IMHO, it's very hard to say.
  • When a chemo incident like the one that happened in KK occurs, there are cries of public remonstration, and the pendulum may swing the other way. Hospitals might make the decision to purchase more expensive and better designed pumps (that is, if they are available). Then years down the road, when a bureaucrat (or a management consultant) with an eye to trim costs looks through the hospital purchasing orders, they may make the suggestion that $XXX could be saved by buying the generic version of such-and-such a product, instead of the more expensive version. And they would not be wrong, just...myopic.Then the cycle starts again.Sometimes it's not only about human factors. It could be about policy, or human nature, or business fundamentals, or just the plain old, dysfunctional way the world works.
    • Weiye Loh
      Weiye Loh on 19 Nov 09
       
      Interesting article. Explains clearly why our 'ethical' considerations is always only limited to a particular context and specific considerations. 
Weiye Loh

A Brief Primer on Criminal Statistics « Canada « Skeptic North - 0 views

www.skepticnorth.com/...-primer-on-criminal-statistics

Crime Statistics

shared by Weiye Loh on 20 Apr 11 - No Cached
  • Occurrences of crime are properly expressed as the number of incidences per 100,000 people. Total numbers are not informative on their own and it is very easy to manipulate an argument by cherry picking between a total number and a rate.  Beware of claims about crime that use raw incidence numbers. When a change in whole incidence numbers is observed, this might not have any bearing on crime levels at all, because levels of crime are dependent on population.
  • Whole Numbers versus Rates
  • Reliability Not every criminal statistic is equally reliable. Even though we have measures of incidences of crimes across types and subtypes, not every one of these statistics samples the actual incidence of these crimes in the same way. Indeed, very few measure the total incidences very reliably at all. The crime rates that you are most likely to encounter capture only crimes known and substantiated by police. These numbers are vulnerable to variances in how crimes become known and verified by police in the first place. Crimes very often go unreported or undiscovered. Some crimes are more likely to go unreported than others (such as sexual assaults and drug possession), and some crimes are more difficult to substantiate as having occurred than others.
  • ...9 more annotations...
  • Complicating matters further is the fact that these reporting patterns vary over time and are reflected in observed trends.   So, when a change in the police reported crime rate is observed from year to year or across a span of time we may be observing a “real” change, we may be observing a change in how these crimes come to the attention of police, or we may be seeing a mixture of both.
  • Generally, the most reliable criminal statistic is the homicide rate – it’s very difficult, though not impossible, to miss a dead body. In fact, homicides in Canada are counted in the year that they become known to police and not in the year that they occurred.  Our most reliable number is very, very close, but not infallible.
  • Crimes known to the police nearly always under measure the true incidence of crime, so other measures are needed to better complete our understanding. The reported crimes measure is reported every year to Statistics Canada from data that makes up the Uniform Crime Reporting Survey. This is a very rich data set that measures police data very accurately but tells us nothing about unreported crime.
  • We do have some data on unreported crime available. Victims are interviewed (after self-identifying) via the General Social Survey. The survey is conducted every five years
  • This measure captures information in eight crime categories both reported, and not reported to police. It has its own set of interpretation problems and pathways to misuse. The survey relies on self-reporting, so the accuracy of the information will be open to errors due to faulty memories, willingness to report, recording errors etc.
  • From the last data set available, self-identified victims did not report 69% of violent victimizations (sexual assault, robbery and physical assault), 62% of household victimizations (break and enter, motor vehicle/parts theft, household property theft and vandalism), and 71% of personal property theft victimizations.
  • while people generally understand that crimes go unreported and unknown to police, they tend to be surprised and perhaps even shocked at the actual amounts that get unreported. These numbers sound scary. However, the most common reasons reported by victims of violent and household crime for not reporting were: believing the incident was not important enough (68%) believing the police couldn’t do anything about the incident (59%), and stating that the incident was dealt with in another way (42%).
  • Also, note that the survey indicated that 82% of violent incidents did not result in injuries to the victims. Do claims that we should do something about all this hidden crime make sense in light of what this crime looks like in the limited way we can understand it? How could you be reasonably certain that whatever intervention proposed would in fact reduce the actual amount of crime and not just reduce the amount that goes unreported?
  • Data is collected at all levels of the crime continuum with differing levels of accuracy and applicability. This is nicely reflected in the concept of “the crime funnel”. All criminal incidents that are ever committed are at the opening of the funnel. There is “loss” all along the way to the bottom where only a small sample of incidences become known with charges laid, prosecuted successfully and responded to by the justice system.  What goes into the top levels of the funnel affects what we can know at any other point later.
Weiye Loh

Johann Hari denies accusations of plagiarism | Media | guardian.co.uk - 0 views

www.guardian.co.uk/...johann-hari-twitter-plagiarism

Journalism Churnalism Plagiarism Objectivity

shared by Weiye Loh on 29 Jun 11 - No Cached
  • "It's clearly not plagiarism or churnalism – but was it an error in another way? Yes. I now see it was wrong, and I wouldn't do it again. I'm grateful to the people who pointed out this error of judgment."
  • when contacted by the Guardian, Levy said he was not unhappy: "I stand behind everything that was published in the interview, which was an accurate representation of my thoughts and words."
  • Hari's interview read: "With a shake of the head, he says: 'We had now two wars, the flotilla – it doesn't seem that Israel has learned any lesson, and it doesn't seem that Israel is paying any price. The Israelis don't pay any price for the injustice of the occupation, so the occupation will never end. It will not end a moment before Israelis understand the connection between the occupation and the price they will be forced to pay. They will never shake it off on their own initiative.'"In July 2007, Levy wrote something very similar in a column for Haaretz: "The Israelis don't pay any price for the injustice of the occupation, so the occupation will never end. It will not end a moment before the Israelis understand the connection between the occupation and the price they will be forced to pay. They will never shake it off on their own initiative, and why should they?"
Weiye Loh

The Real Hoax Was Climategate | Media Matters Action Network - 0 views

politicalcorrection.org/...201007020005

Climate Science ClimateGate

shared by Weiye Loh on 04 Jul 10 - Cached
  • Sen. Jim Inhofe's (R-OK) biggest claim to fame has been his oft-repeated line that global warming is "the greatest hoax ever perpetrated on the American people."
  • In 2003, he conceded that the earth was warming, but denied it was caused by human activity and suggested that "increases in global temperatures may have a beneficial effect on how we live our lives."
  • In 2009, however, he appeared on Fox News to declare that the earth was actually cooling, claiming "everyone understands that's the case" (they don't, because it isn't).
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  • nhofe's battle against climate science kicked into overdrive when a series of illegally obtained emails surfaced from the Climatic Research Unit at East Anglia University. 
  • When the dubious reports surfaced about flawed science, manipulated data, and unsubstantiated studies, Inhofe was ecstatic.  In March, he viciously attacked former Vice President Al Gore for defending the science behind climate change
  • Unfortunately for Senator Inhofe, none of those things are true.  One by one, the pillars of evidence supporting the alleged "scandals" have shattered, causing the entire "Climategate" storyline to come crashing down. 
  • a panel established by the University of East Anglia to investigate the integrity of the research of the Climatic Research Unit wrote: "We saw no evidence of any deliberate scientific malpractice in any of the work of the Climatic Research Unit and had it been there we believe that it is likely that we would have detected it."
  • Responding to allegations that Dr. Michael Mann tampered with scientific evidence, Pennsylvania State University conducted a thorough investigation. It concluded: "The Investigatory Committee, after careful review of all available evidence, determined that there is no substance to the allegation against Dr. Michael E. Mann, Professor, Department of Meteorology, The Pennsylvania State University.  More specifically, the Investigatory Committee determined that Dr. Michael E. Mann did not engage in, nor did he participate in, directly or indirectly, any actions that seriously deviated from accepted practices within the academic community for proposing, conducting, or reporting research, or other scholarly activities."
  • London's Sunday Times retracted its story, echoed by dozens of outlets, that an IPCC issued an unsubstantiated report claiming 40% of the Amazon rainforest was endangered due to changing rainfall patterns.  The Times wrote: "In fact, the IPCC's Amazon statement is supported by peer-reviewed scientific evidence. In the case of the WWF report, the figure had, in error, not been referenced, but was based on research by the respected Amazon Environmental Research Institute (IPAM) which did relate to the impact of climate change."
  • The Times also admitted it misrepresented the views of Dr. Simon Lewis, a Royal Society research fellow at the University of Leeds, implying he agreed with the article's false premise and believed the IPCC should not utilize reports issued by outside organizations.  In its retraction, the Times was forced to admit: "Dr Lewis does not dispute the scientific basis for both the IPCC and the WWF reports," and, "We accept that Dr Lewis holds no such view... A version of our article that had been checked with Dr Lewis underwent significant late editing and so did not give a fair or accurate account of his views on these points. We apologise for this."
  • Weiye Loh on 04 Jul 10
     
    The Real Hoax Was Climategate July 02, 2010 1:44 pm ET by Chris Harris
Weiye Loh

Hermits and Cranks: Lessons from Martin Gardner on Recognizing Pseudoscientists: Scient... - 0 views

www.scientificamerican.com/article.cfm

Science Pseudo-Science

shared by Weiye Loh on 09 Jul 10 - Cached
  • In 1950 Martin Gardner published an article in the Antioch Review entitled "The Hermit Scientist," about what we would today call pseudoscientists.
  • there has been some progress since Gardner offered his first criticisms of pseudoscience. Now largely antiquated are his chapters on believers in a flat Earth, a hollow Earth, Atlantis and Lemuria, Alfred William Lawson, Roger Babson, Trofim Lysenko, Wilhelm Reich and Alfred Korzybski. But disturbingly, a good two thirds of the book's contents are relevant today, including Gardner's discussions of homeopathy, naturopathy, osteopathy, iridiagnosis (reading the iris of the eye to deter- mine bodily malfunctions), food faddists, cancer cures and other forms of medical quackery, Edgar Cayce, the Great Pyramid's alleged mystical powers, handwriting analysis, ESP and PK (psychokinesis), reincarnation, dowsing rods, eccentric sexual theories, and theories of group racial differences.
  • The "hermit scientist," a youthful Gardner wrote, works alone and is ignored by mainstream scientists. "Such neglect, of course, only strengthens the convictions of the self-declared genius."
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  • Even then Gardner was bemoaning that some beliefs never seem to go out of vogue, as he recalled an H. L. Mencken quip from the 1920s: "Heave an egg out of a Pullman window, and you will hit a Fundamentalist almost anywhere in the U.S. today." Gardner cautions that when religious superstition should be on the wane, it is easy "to forget that thousands of high school teachers of biology, in many of our southern states, are still afraid to teach the theory of evolution for fear of losing their jobs." Today creationism has spread northward and mutated into the oxymoronic form of "creation science."
  • the differences between science and pseudoscience. On the one extreme we have ideas that are most certainly false, "such as the dianetic view that a one-day-old embryo can make sound recordings of its mother's conversation." In the borderlands between the two "are theories advanced as working hypotheses, but highly debatable because of the lack of sufficient data." Of these Gardner selects a most propitious propitious example: "the theory that the universe is expanding." That theory would now fall at the other extreme end of the spectrum, where lie "theories al- most certainly true, such as the belief that the Earth is round or that men and beasts are distant cousins."
  • How can we tell if someone is a scientific crank? Gardner offers this advice: (1) "First and most important of these traits is that cranks work in almost total isolation from their colleagues." Cranks typically do not understand how the scientific process operates—that they need to try out their ideas on colleagues, attend conferences and publish their hypotheses in peer-reviewed journals before announcing to the world their startling discovery. Of course, when you explain this to them they say that their ideas are too radical for the conservative scientific establishment to accept.
  • (2) "A second characteristic of the pseudo-scientist, which greatly strengthens his isolation, is a tendency toward paranoia," which manifests itself in several ways: (1) He considers himself a genius. (2) He regards his colleagues, without exception, as ignorant blockheads....(3) He believes himself unjustly persecuted and discriminated against. The recognized societies refuse to let him lecture. The journals reject his papers and either ignore his books or assign them to "enemies" for review. It is all part of a dastardly plot. It never occurs to the crank that this opposition may be due to error in his work....(4) He has strong compulsions to focus his attacks on the greatest scientists and the best-established theories. When Newton was the outstanding name in physics, eccentric works in that science were violently anti-Newton. Today, with Einstein the father-symbol of authority, a crank theory of physics is likely to attack Einstein....(5) He often has a tendency to write in a complex jargon, in many cases making use of terms and phrases he himself has coined.
  • "If the present trend continues," Gardner concludes, "we can expect a wide variety of these men, with theories yet unimaginable, to put in their appearance in the years immediately ahead. They will write impressive books, give inspiring lectures, organize exciting cults. They may achieve a following of one—or one million. In any case, it will be well for ourselves and for society if we are on our guard against them."
  • Weiye Loh on 09 Jul 10
     
    May 23, 2010 | 31 comments Hermits and Cranks: Lessons from Martin Gardner on Recognizing Pseudoscientists Fifty years ago Gardner launched the modern skeptical movement. Unfortunately, much of what he wrote about is still current today By Michael Shermer   
Weiye Loh

Rationally Speaking: Should non-experts shut up? The skeptic's catch-22 - 0 views

rationallyspeaking.blogspot.com/...perts-shut-up-skeptics_14.html

Science Communication skepticism

shared by Weiye Loh on 17 Jul 10 - Cached
  • You can read the talk here, but in a nutshell, Massimo was admonishing skeptics who reject the scientific consensus in fields in which they have no technical expertise - the most notable recent example of this being anthropogenic climate change, about which venerable skeptics like James Randi and Michael Shermer have publicly expressed doubts (though Shermer has since changed his mind).
  • I'm totally with Massimo that it seems quite likely that anthropogenic climate change is really happening. But I'm not sure I can get behind Massimo's broader argument that non-experts should defer to the expert consensus in a field.
  • First of all, while there are strong incentives for a researcher to find errors in other work in the field, there are strong disincentives for her to challenge the field's foundational assumptions. It will be extremely difficult for her to get other people to agree with her if she tries, and if she succeeds, she'll still be taking herself down along with the rest of the field.
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  • 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.
  • Weiye Loh on 17 Jul 10
     
    WEDNESDAY, JULY 14, 2010 Should non-experts shut up? The skeptic's catch-22
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