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Merchant of Doubt is exactly what its subtitle says: a historical view of how a handful of scientists have obscured the truth on matters of scientific fact.

it was a very small group who were responsible for creating a great deal of doubt on a variety of issues. The book opens in 1953, where the tobacco industry began to take action to obscure the truth about smoking’s harmful effects, when its relationship to cancer first received widespread media attention.

The tobacco industry exploited scientific tendency to be conservative in drawing conclusions, to throw up a handful of cherry-picked data and misleading statistics and to “spin unreasonable doubt.” This tactic, combined with the media’s adherence to the “fairness doctrine” which was interpreted as giving equal time “to both sides [of an issue], rather than giving accurate weight to both sides” allowed the tobacco industry to delay regulation for decades.

Science as a public enterprise: opening up scientific information will look at how scientific information should best be managed to improve the quality of research and build public trust.

“Science has always been about open debate. But incidents such as the UEA email leaks have prompted the Royal Society to look at how open science really is.  With the advent of the Internet, the public now expect a greater degree of transparency. The impact of science on people’s lives, and the implications of scientific assessments for society and the economy are now so great that  people won’t just believe scientists when they say “trust me, I’m an expert.” It is not just scientists who want to be able to see inside scientific datasets, to see how robust they are and ask difficult questions about their implications. Science has to adapt.”

The study will look at questions such as: What are the benefits and risks of openly sharing scientific data? How does the rise of the blogosphere change scientific research? What responsibility should scientists, their institutions and the funders of research have for open data? How do we make information more accessible and who will pay to do it? Should privately funded scientists be held to the same standards as those who are publicly funded? How do we balance openness against intellectual property rights and in the case of medical information how do protect patient confidentiality?  Will the same rules apply to scientists across the world?

pharmaceutical company Merck, Sharp & Dohme paid Elsevier to produce a fake medical journal that, to any superficial examination, looked like a real medical journal but was in reality nothing more than advertising for Merck

As reported by The Scientist: Merck paid an undisclosed sum to Elsevier to produce several volumes of a publication that had the look of a peer-reviewed medical journal, but contained only reprinted or summarized articles--most of which presented data favorable to Merck products--that appeared to act solely as marketing tools with no disclosure of company sponsorship. "I've seen no shortage of creativity emanating from the marketing departments of drug companies," Peter Lurie, deputy director of the public health research group at the consumer advocacy nonprofit Public Citizen, said, after reviewing two issues of the publication obtained by The Scientist. "But even for someone as jaded as me, this is a new wrinkle." The Australasian Journal of Bone and Joint Medicine, which was published by Exerpta Medica, a division of scientific publishing juggernaut Elsevier, is not indexed in the MEDLINE database, and has no website (not even a defunct one). The Scientist obtained two issues of the journal: Volume 2, Issues 1 and 2, both dated 2003. The issues contained little in the way of advertisements apart from ads for Fosamax, a Merck drug for osteoporosis, and Vioxx.

there are numerous "throwaway" journals out there. "Throwaway" journals tend to be defined as journals that are provided free of charge, have a lot of advertising (a high "advertising-to-text" ratio, as it is often described), and contain no original investigations. Other relevant characteristics include: Supported virtually entirely by advertising revenue. Ads tend to be placed within article pages interrupting the articles, rather than between articles, as is the case with most medical journals that accept ads Virtually the entire content is reviews of existing content of variable (and often dubious) quality. Parasitic. Throwaways often summarize peer-reviewed research from real journals. Questionable (at best) peer review. Throwaways tend to cater to an uninvolved and uncritical readership. No original work.

Mass vaccination completely eradicated smallpox, which had been killing one in seven children.  Public health campaigns have also eliminated diptheria, and reduced the incidence of pertussis, tetanus, measles, rubella and mumps to near zero.

when vaccination rates drop, diseases can reemerge in the population again. Measles is currently endemic in the United Kingdom, after vaccination rates dropped below 80%. When diptheria immunization dropped in Russia and Ukraine in the early 1990′s, there were over 100,000 cases with 1,200 deaths.  In Nigeria in 2001, unfounded fears of the polio vaccine led to a drop in vaccinations, an re-emergence of infection, and the spread of polio to ten other countries.

one reason that has experienced a dramatic upsurge over the past decade or so has been the fear that vaccines cause autism. The connection between autism and vaccines, in particular the measles, mumps, rubella (MMR) vaccine, has its roots in a paper published by Andrew Wakefield in 1998 in the medical journal The Lancet.  This link has already been completely and thoroughly debunked – there is no evidence to substantiate this connection. But over the past two weeks, the full extent of the deception propagated by Wakefield was revealed. The British Medical Journal has a series of articles from journalist Brian Deer (part 1, part 2), who spent years digging into the facts behind Wakefield,  his research, and the Lancet paper

That's the title of The Atlantic profile of Dr. John Ioannidis who "has spent his career challenging his peers by exposing their bad science." His 2005 paper in PLoS Medicine was on why most published research findings are false.

Ioannidis anticipated that the community might shrug off his findings: sure, a lot of dubious research makes it into journals, but we researchers and physicians know to ignore it and focus on the good stuff, so what’s the big deal? The other paper headed off that claim.

He zoomed in on 49 of the most highly regarded research findings in medicine over the previous 13 years, as judged by the science community’s two standard measures: the papers had appeared in the journals most widely cited in research articles, and the 49 articles themselves were the most widely cited articles in these journals.

This year will fundamentally change how we think about and buy access to prospects, namely keywords. It is the dawn of paid search without keywords.

Google's search results were dominated by the "10 blue links" -- simple headlines, descriptions, and URLs to entice and satisfy searchers. Until it wasn't. Universal search wove in images, video, and real-time updates.

For most of its history, too, AdWords been presented in a text format even as the search results morphed into a multimedia experience. The result is that attention was pulled towards organic results at the expense of ads.

Despite the revolutionary technological advance of the printing press in the 15th century, there is precious little economic evidence of its benefits. Using data on 200 European cities between 1450 and 1600, this column finds that economic growth was higher by as much as 60 percentage points in cities that adopted the technology.

Historians argue that the printing press was among the most revolutionary inventions in human history, responsible for a diffusion of knowledge and ideas, “dwarfing in scale anything which had occurred since the invention of writing” (Roberts 1996, p. 220). Yet economists have struggled to find any evidence of this information technology revolution in measures of aggregate productivity or per capita income (Clark 2001, Mokyr 2005). The historical data thus present us with a puzzle analogous to the famous Solow productivity paradox – that, until the mid-1990s, the data on macroeconomic productivity showed no effect of innovations in computer-based information technology.

In recent work (Dittmar 2010a), I examine the revolution in Renaissance information technology from a new perspective by assembling city-level data on the diffusion of the printing press in 15th-century Europe. The data record each city in which a printing press was established 1450-1500 – some 200 out of over 1,000 historic cities (see also an interview on this site, Dittmar 2010b). The research emphasises cities for three principal reasons. First, the printing press was an urban technology, producing for urban consumers. Second, cities were seedbeds for economic ideas and social groups that drove the emergence of modern growth. Third, city sizes were historically important indicators of economic prosperity, and broad-based city growth was associated with macroeconomic growth (Bairoch 1988, Acemoglu et al. 2005).

Chinese citizens are busy dissecting and taunting the meeting on social media. While Premier Wen Jiabao was pledging that the government would “quickly” reverse the widening gap between rich and poor—last year he said it would do so “gradually”—Chinese Web users were scrutinizing photos of delegates arriving for the meeting, and posting photos of their nine-hundred dollar Hermès belts and Birkin and Celine and Louis Vuitton purses that retail for car prices. As Danwei points out, an image that has been making the rounds with particular relish shows the C.E.O. of China Power International Development Ltd, Li Xiaolin, in a salmon-colored suit from Emilio Pucci’s spring-summer 2012 collection—price: nearly two thousand dollars. Web user Cairangduoji paired her photo with the image of dirt-covered barefoot kids in the countryside and the comment: “That amount could help two hundred children wear warm clothes, and avoid the chilly attacks of winter.” And it appended a quote from Li, of the salmon suit, who purportedly once said, “I think we should open a morality file on all citizens to control everyone and give them a ‘sense of shame.’” (This is no ordinary delegate: Li Xiaolin happens to be the daughter of former Premier Li Peng, who oversaw the crackdown at Tiananmen Square.)

Another message making the rounds uses an official high-res photo of the gathering to zoom in on delegates who were captured fast asleep or typing on their smart phones.

As an author, I’ve seen how the sales histories of textbooks work. Typically they have a big spike of sales for the first 1-2 years after they are introduced, and that’s when most the new copies are sold and most of the publisher’s money is made. But by year 3  (and sometimes sooner), the sales plunge and within another year or two, the sales are miniscule. The publishers have only a few options in a situation like this. One option: they can price the book so that the first two years’ worth of sales will pay their costs back before the used copies wipe out their market, which is the major reason new copies cost so much. Another option (especially with high-volume introductory textbooks) is to revise it within 2-3 years after the previous edition, so the new edition will drive all the used copies off the shelves for another two years or so. This is also a common strategy. For my most popular books, the publisher expected me to be working on a new edition almost as soon as the previous edition came out, and 2-3 years later, the new edition (with a distinctive new cover, and sometimes with significant new content as well) starts the sales curve cycle all over again. One of my books is in its eighth edition, but there are introductory textbooks that are in the 15th or 20th edition.

For over 20 years now, I’ve heard all sorts of prophets saying that paper textbooks are dead, and predicting that all textbooks would be electronic within a few years. Year after year, I  hear this prediction—and paper textbooks continue to sell just fine, thank you.  Certainly, electronic editions of mass market best-sellers, novels and mysteries (usually cheaply produced with few illustrations) seem to do fine as Kindle editions or eBooks, and that market is well established. But electronic textbooks have never taken off, at least in science textbooks, despite numerous attempts to make them work. Watching students study, I have a few thoughts as to why this is: Students seem to feel that they haven’t “studied” unless they’ve covered their textbook with yellow highlighter markings. Although there are electronic equivalents of the highlighter marker pen, most of today’s students seem to prefer physically marking on a real paper book. Textbooks (especially science books) are heavy with color photographs and other images that don’t often look good on a tiny screen, don’t print out on ordinary paper well, but raise the price of the book. Even an eBook is going to be a lot more expensive with lots of images compared to a mass-market book with no art whatsoever. I’ve watched my students study, and they like the flexibility of being able to use their book just about anywhere—in bright light outdoors away from a power supply especially. Although eBooks are getting better, most still have screens that are hard to read in bright light, and eventually their battery will run out, whether you’re near a power supply or not. Finally, if  you drop your eBook or get it wet, you have a disaster. A textbook won’t even be dented by hard usage, and unless it’s totally soaked and cannot be dried, it does a lot better when wet than any electronic book.

A recent study found that digital textbooks were no panacea after all. Only one-third of the students said they were comfortable reading e-textbooks, and three-fourths preferred a paper textbook to an e-textbook if the costs were equal. And the costs have hidden jokers in the deck: e-textbooks may seem cheaper, but they tend to have built-in expiration dates and cannot be resold, so they may be priced below paper textbooks but end up costing about the same. E-textbooks are not that much cheaper for publishers, either, since the writing, editing, art manuscript, promotion, etc., all cost the publisher the same whether the final book is in paper or electronic. The only cost difference is printing and binding and shipping and storage vs. creating the electronic version.

My recent post “The War Over ‘Nice’” (describing the blogosphere’s reaction to Phil Plait’s “Don’t Be a Dick” speech) has topped out at more than 200 comments.

Many readers appear to object (some strenuously) to the very ideas of discussing best practices, seeking evidence of efficacy for skeptical outreach, matching strategies to goals, or encouraging some methods over others. Some seem to express anger that a discussion of best practices would be attempted at all. 

No Right or Wrong Way? The milder forms of these objections run along these lines: “Everyone should do their own thing.” “Skepticism needs all kinds of approaches.” “There’s no right or wrong way to do skepticism.” “Why are we wasting time on these abstract meta-conversations?”

a journal's impact factor for a given year is the average number of citations received by papers published in the journal during the two preceding years. Letters to the editor, editorials, book reviews, and other non-papers are excluded from the impact factor calculation.

Review papers that don't necessarily contain new scientific knowledge yet provide useful overviews garner lots of citations. Five of the top 10 perennially highest-impact-factor journals, including the top four, are review journals.

Now suppose you're a journal editor or publisher. In these tough financial times, cash-strapped libraries use impact factors to determine which subscriptions to keep and which to cancel. How would you raise your journal's impact factor? Publishing fewer and better papers is one method. Or you could run more review articles. But, as a paper posted recently on arXiv describes, there's another option: You can manipulate the impact factor by publishing your own papers that cite your own journal.

In 2001, rumors were circulating in Greek hospitals that surgery residents, eager to rack up scalpel time, were falsely diagnosing hapless Albanian immigrants with appendicitis. At the University of Ioannina medical school’s teaching hospital, a newly minted doctor named Athina Tatsioni was discussing the rumors with colleagues when a professor who had overheard asked her if she’d like to try to prove whether they were true—he seemed to be almost daring her. She accepted the challenge and, with the professor’s and other colleagues’ help, eventually produced a formal study showing that, for whatever reason, the appendices removed from patients with Albanian names in six Greek hospitals were more than three times as likely to be perfectly healthy as those removed from patients with Greek names. “It was hard to find a journal willing to publish it, but we did,” recalls Tatsioni. “I also discovered that I really liked research.” Good thing, because the study had actually been a sort of audition. The professor, it turned out, had been putting together a team of exceptionally brash and curious young clinicians and Ph.D.s to join him in tackling an unusual and controversial agenda.

were drug companies manipulating published research to make their drugs look good? Salanti ticked off data that seemed to indicate they were, but the other team members almost immediately started interrupting. One noted that Salanti’s study didn’t address the fact that drug-company research wasn’t measuring critically important “hard” outcomes for patients, such as survival versus death, and instead tended to measure “softer” outcomes, such as self-reported symptoms (“my chest doesn’t hurt as much today”). Another pointed out that Salanti’s study ignored the fact that when drug-company data seemed to show patients’ health improving, the data often failed to show that the drug was responsible, or that the improvement was more than marginal.

This paper is desperate. Please reject it completely and then block the author's email ID so they can't use the online system in future.

The type of lava vs. diversity has no meaning if only one of each sample is analyzed; multiple samples are required for generality. This controls provenance (e.g. maybe some beetle took a pee on one or the other of the samples, seriously skewing relevance to lava composition).

Merry X-mas! First, my recommendation was reject with new submission, because it is necessary to investigate further, but reading a well written manuscript before X-mas makes me feel like Santa Claus.

There are two new papers in Nature this week that go right to the heart of the conversation about extreme events and their potential relationship to climate change.

Let’s start with some very basic, but oft-confused points: Not all extremes are the same. Discussions of ‘changes in extremes’ in general without specifying exactly what is being discussed are meaningless. A tornado is an extreme event, but one whose causes, sensitivity to change and impacts have nothing to do with those related to an ice storm, or a heat wave or cold air outbreak or a drought. There is no theory or result that indicates that climate change increases extremes in general. This is a corollary of the previous statement – each kind of extreme needs to be looked at specifically – and often regionally as well. Some extremes will become more common in future (and some less so). We will discuss the specifics below. Attribution of extremes is hard. There are limited observational data to start with, insufficient testing of climate model simulations of extremes, and (so far) limited assessment of model projections.

The two new papers deal with the attribution of a single flood event (Pall et al), and the attribution of increased intensity of rainfall across the Northern Hemisphere (Min et al). While these issues are linked, they are quite distinct, and the two approaches are very different too.

Scientists these days tend to keep up a polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist's field and methods of study are as good as every other scientist's and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants.

Why should there be such rapid advances in some fields and not in others? I think the usual explanations that we tend to think of - such as the tractability of the subject, or the quality or education of the men drawn into it, or the size of research contracts - are important but inadequate. I have begun to believe that the primary factor in scientific advance is an intellectual one. These rapidly moving fields are fields where a particular method of doing scientific research is systematically used and taught, an accumulative method of inductive inference that is so effective that I think it should be given the name of "strong inference." I believe it is important to examine this method, its use and history and rationale, and to see whether other groups and individuals might learn to adopt it profitably in their own scientific and intellectual work. In its separate elements, strong inference is just the simple and old-fashioned method of inductive inference that goes back to Francis Bacon. The steps are familiar to every college student and are practiced, off and on, by every scientist. The difference comes in their systematic application. Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: Devising alternative hypotheses; Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly is possible, exclude one or more of the hypotheses; Carrying out the experiment so as to get a clean result; Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.

On any new problem, of course, inductive inference is not as simple and certain as deduction, because it involves reaching out into the unknown. Steps 1 and 2 require intellectual inventions, which must be cleverly chosen so that hypothesis, experiment, outcome, and exclusion will be related in a rigorous syllogism; and the question of how to generate such inventions is one which has been extensively discussed elsewhere (2, 3). What the formal schema reminds us to do is to try to make these inventions, to take the next step, to proceed to the next fork, without dawdling or getting tied up in irrelevancies.

The problem of replicability in science from xkcdby Massimo Pigliucci

In recent months much has been written about the apparent fact that a surprising, indeed disturbing, number of scientific findings cannot be replicated, or when replicated the effect size turns out to be much smaller than previously thought.

Arguably, the recent streak of articles on this topic began with one penned by David Freedman in The Atlantic, and provocatively entitled “Lies, Damned Lies, and Medical Science.” In it, the major character was John Ioannidis, the author of some influential meta-studies about the low degree of replicability and high number of technical flaws in a significant portion of published papers in the biomedical literature.

A reporter emailed me this morning to see if I could answer a few questions about poverty. Sure I said. The emailed questions that followed?It is realistic to think that poverty can one day end?What, in your view, are the best global solutions?How urgent is it to act (in the context of climate change)?

My first reaction: thanks for asking the easy questions, lady. Was this serious? How can one possibly answer the grand questions of development in a few sentences?

It is realistic to think that poverty can one day end?In America, you can be poor but own a car, a television, and have food on the table every day. In northern Uganda, that would make you a very wealthy man.Do I see a world where nearly every household has their basic needs covered, plus some of the comforts of life? Absolutely. I imagine most places on the planet will get to what we now think of as middle-income status—perhaps $8,000 to $14,000 per head in 2011 dollars and purchasing ability. The poorest nations will probably be in those places least advantageous to trade (the landlocked, for instance) and where cultures or political systems restrict innovation and freedoms.But poverty is a relative measure, and short of a Star Trek world where you can summon food and items out of a wall unit, there will always be people who struggle to keep up.

but News Feed only displays a subset of the stories generated by your friends — if it displayed everything, there’s a good chance you’d be overwhelmed. Developers are always trying to make sure their sites and apps are publishing stories that make the cut, which has led to the concept of “News Feed Optimization”, and their success is dictated by EdgeRank.

At a high level, the EdgeRank formula is fairly straightforward. But first, some definitions: every item that shows up in your News Feed is considered an Object. If you have an Object in the News Feed (say, a status update), whenever another user interacts with that Object they’re creating what Facebook calls an Edge, which includes actions like tags and comments. Each Edge has three components important to Facebook’s algorithm: First, there’s an affinity score between the viewing user and the item’s creator — if you send your friend a lot of Facebook messages and check their profile often, then you’ll have a higher affinity score for that user than you would, say, an old acquaintance you haven’t spoken to in years. Second, there’s a weight given to each type of Edge. A comment probably has more importance than a Like, for example. And finally there’s the most obvious factor — time. The older an Edge is, the less important it becomes.

Multiply these factors for each Edge then add the Edge scores up and you have an Object’s EdgeRank. And the higher that is, the more likely your Object is to appear in the user’s feed. It’s worth pointing out that the act of creating an Object is also considered an Edge, which is what allows Objects to show up in your friends’ feeds before anyone has interacted with them.

I refer to the article “Straits Times! Why you edit until like that?” (theonlinecitizen, Mar 24). In my view, the Straits Times Forum Editor was not wrong to edit the letter.

From a statistical pespective, the forum letter writer, Mr Samuel Wee, was quoting the wrong statistics.

For example, the Education Minister said “How children from the bottom one-third by socio-economic background fare: One in two scores in the top two-thirds at PSLE” - But, Mr Samuel Wee wrote “His 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”. Another example is Mr Wee’s: “it is indeed heartwarming to learn that only 90% of children from one-to-three-room flats do not make it to university”, when the Straits Times article “New chapter in the Singapore Story”http://pdfcast.org/pdf/new-chapter-in-singapore-story of 8 March, on the Minister’s speech in Parliament, clearly showed in the graph “Progression to Unis and Polys” (Source: MOE  (Ministry of Eduction)), that the “percentage of P1 pupils who lived in 1- to 3-room HDB flats and subsequently progressed to tertiary education”, was about 50 per cent, and not the ’90 per cent who do not make it’ cited by Mr Samuel Wee.