Evidence Soup

Does the evidence show that Dunbar's number really matters? Or are weak ties just as good as strong ones?

Contributors to the Smart Data Collective forum frequently offer useful insights about analytics and other topics. I liked a recent piece by Jacob Morgan about Dunbar's number: According to Wikipedia, this is a "theoretical cognitive limit to the number of people with whom one can maintain stable social relationships." Dunbar's is typically believed to be about 150. Makes sense to me - especially when I think about the circle of people I stay in regular contact with, invite to parties, interact with on Facebook, etc.

I'm intrigued by social network research: The structural holes stuff, etc. Lots of folks are exploring how people make connections, and what difference these connections (or lack thereof) can make. Of course, I'm most interested in understanding the role of social networks in spreading useful evidence as quickly as possible.

Strong vs. weak. Dunbar's number describes what many folks call "strong ties". Is a strong connection required to get people to consider new evidence, especially when that evidence challenges their current beliefs? Or are weak ties enough?

Morten T. Hansen, in his book Collaboration: How Leaders Avoid the Traps, Create Unity, and Reap Big Results, talks about the importance of weak ties. "[R]esearch shows that weak ties can prove much more helpful in networking, because they form bridges to worlds we do not walk within. Strong ties, on the other hand, tend to be worlds we already know; a good friends often knows many of the same people and things we know." And he offers a pragmatic explanation of why your weak-tie number can go much higher than 150: "It’s less time consuming to talk to someone once a month (weak tie) than twice a week (a strong tie). People can keep up quite a few weak ties without them being a burden."

Authority matters, too. When we consider new evidence, the authority of the source is important - this is a key component of making something persuasive. Technology has made it much easier to learn about new evidence and locate new authorities, even when we have weak - or possibly nonexistent - ties to those sources. (Consider Twitter. Evidence often comes from people you're following, or people who are re-Tweeted, even if you don't know them.)

Beating the number. For those who do want to be strongly connected to as many people as possible, Chris Brogan writes about "beating Dunbar's number" - for instance, by establishing yourself in multiple groups based on specific geography, a specific industry vertical, etc. Some of his points are a different way of saying "it's not who you know, it's who knows you" - and I agree with him on that. Brogan writes: "[M]ake it work in reverse. By that, I mean this: if people think of you as part of their 150, then they come to you, and they seek you out. This comes from having some value to offer. If, for instance, you’re helpful in finding others business, those people will seek you out the next time they have an opportunity, or the next time they need your help. In either case, you’re in the network."

The bottom line? I think Dunbar's number is useful for evaluating a certain type of connection: The ones characterized by strong ties. But for spreading evidence to a wider audience, it's not important. You can successfully communicate evidence to folks with whom you have only weak ties.

By the way, Dunbar has a book called Grooming, Gossip, and the Evolution of Language. I've not read it, but the reviews are quite positive, and I've heard it's a worthwhile read.

Very old evidence is both awesome and hilarious.

Wired has a nice writeup about some old scientific publications in Ground-Breaking Science: Very Old Papers Are Both Awesome and Hilarious. "...history-making papers published by the United Kingdom’s Royal Society [are] released in their entirety to celebrate the 350th birthday of the world’s oldest scientific body. The 60 papers are a testament to human curiosity, and the power of ingenuity and rigorous observation to overcome ignorance." Some examples:

Hilarity:
1666: Tryals Proposed by Mr. Boyle to Dr. Lower, to be Made by Him, for the Improvement of Transfusing Blood out of One Live Animal into Another. "...this suggested that the nature of organismal character might be revealed by swapping blood between dogs. [Wondering] if 'a fierce Dog, by being often quite new stocked with the blood of a cowardly Dog, may not become more tame'?" (For the record, my incredibly good-natured boy Petey is not amused by this discussion.)

Awesomeness:
1965: The Fit of the Continents Around the Atlantic.

"Even though man was about to walk on the moon, the idea that continents drifted across Earth’s surface was still controversial. In this paper, Edward Bullard showed how neatly the continents fit together, from their shape to common properties of rocks and fossils. Plate tectonics is now widely accepted, and an instructive reminder of how human knowledge is continually under construction."

Sage is giving away the evidence for free (this month only).

Sage Journals Online is offering free access to 250,000+ articles from 500 of its journals. Everything from Anthropology & Archeology to Information Science to Vascular Medicine & Surgery.

The program ends October 31. You can register here.

Evidence shows that we like it when people ooze confidence. Even if they're wrong.

In May, at the Association for Psychological Science session Often in Error, Rarely in Doubt, researchers presented findings showing that "Excessive confidence in the precision of one’s knowledge is both the most robust and the least understood form of overconfidence. This symposium investigates its ultimate causes. The evidence suggests that overprecision is caused by limitations on the working capacity in human memory, conversational norms, and social pressure."

Gold star. I must say, psychologists are pretty good at explaining their evidence to us regular folk. Their research abstracts, and even some of their academic papers, are actually understandable -- so kudos to them. Presentations at the Rarely in Doubt session included:

Overconfidence and the Representation of Uncertainty (Jack B. Soll, Duke University). "We compare uncertainty about repeatable cases (e.g., a random person’s weight) with uncertainty about specific cases (e.g., Rush Limbaugh’s weight). We find that perceptions of repeatable cases are noisy but accurate. In contrast, for specific cases people underestimate the variability around one’s best guess — they are overconfident in their answers."

Competing To Be Certain (But Wrong): Social Pressure and Overprecision in Judgment (Joseph R. Radzevick and Don A. Moore, Carnegie Mellon University and University of California, Berkeley). "We find that the competitive social pressure of a market contributes to overprecision among those competing for influence. When advisors must compete with each other, we find that the confidence they claim escalates over time. This overprecision helps advisors’ sell their advice."

Here's a draft of their paper (36-page pdf). The abstract says "Overly precise judgments claim more certainty than is objectively warranted. In this paper, we investigate whether the competitive social pressure of a market contributes to overprecision among those competing for influence. We find evidence that markets do indeed exacerbate overprecision. This evidence comes from two experiments in which advisors attempt to sell their advice. In the first experiment, advisors must compete with other advice sellers. In the second, advisors and decision makers are paired. Overprecision exists in both studies, and it helps advisors’ sell their advice."

New Scientist wrote a nice recap of Radzevick and Moore's findings in Humans Prefer Cockiness to Expertise, saying "The research... shows that we prefer advice from a confident source, even to the point that we are willing to forgive a poor track record.... [In] competitive situations, this can drive those offering advice to increasingly exaggerate how sure they are. And it spells bad news for scientists who try to be honest about gaps in their knowledge."

You're so cute when you're mad. The evidence says you're more optimistic, and more of a risk-taker, when angry.

On Science Friday this week, I happened to overhear this great research title: Portrait of The Angry Decision Maker. The SciFri show was about the science of decision making, and the podcast is worth a listen. Jennifer Lerner (Harvard) is an entertaining radio guest. She co-authored the Portrait paper along with Larissa Tiedens (Stanford). The researchers say their findings indicate that anger has different effects than other negative emotions: We take bigger risks when angry, and are more optimistic about probable outcomes.

Is anger always a bad thing? This had me wondering if anger is really a negative emotion -- I've seen business-related articles about people starting companies when motivated by anger (anger over bureaucracy at their current job, for example). There's certainly nothing wrong with keeping a clear head -- and I'm definitely not advocating TWA (Texting While Angry) -- but I believe a little anger can be a good thing. Lo and behold, when I looked at the article online, the authors were wondering the same thing, asking "whether anger should be considered a positive emotion."

The full title of the research is Portrait of The Angry Decision Maker: How Appraisal Tendencies Shape Anger's Influence on Cognition (pdf here, and html version here). It offers empirical evidence about anger's "unique relationship top judgment and decision-making". Portrait also "proposes the hypothesis that anger will be experienced as relatively unpleasant and unrewarding when reflecting back on the source of one’s anger but experienced as relatively pleasant and rewarding when looking forward." Sounds about right.

Cory Doctorow on Boing Boing liked Portrait also, saying it's a "fascinating look at the role of irrationality on decision-making, and a damning rebuttal of the idea of rationality in the marketplace".

(BTW, I love the name of Doctorow's site, Craphound.)

More evidence about social networks.

Responding to my recent post about influencers in social networks (The anti-Malcolm Gladwell speaks), Rick Niederman at the Forsyth Institute sent some useful research references, saying "you may want to consider the work of Steve Borgatti (U of KY, school of business) and Tom Valente (USC medical school).... Steve has a recent Science article on this [and] Tom has 2 textbooks." Thanks, Rick.

Interesting stuff. I'm fascinated by graphs and social network analysis, especially how they pertain to dissemination of evidence and knowledge (graph theory was my favorite math course). So I looked up the Science article, Network Analysis in the Social Sciences. It's by Stephen P. Borgatti, Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca (13-Feb-2009, pp 892-895). Their abstract says "For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field." I recommend this well-written article for anyone who wants a basic understanding of how network analysis is applied to social science.

Social capital and consequences. The authors note that a "fundamental axiom in social network research is that a node's position in a network determines in part the opportunities and constraints that it encounters, and in this way plays an important role in a node's outcomes. This is the network thinking behind the popular concept of social capital, which in one formulation posits that the rate of return on an actor's investment in their human capital (i.e., their knowledge, skills, and abilities) is determined by their social capital (i.e., their network location)."

[Fig. 4. Two illustrative ego networks. The one on the left contains many structural holes; the one on the right contains few. From Science, Network Analysis in the Social Sciences.]

Theoretical mechanisms and egos. The article reviews the current thinking on what makes things -- money, an idea, or something else -- flow from one node to another. "Perhaps the most common mechanism for explaining consequences of social network variables is some form of direct transmission from node to node." Researchers have examined various mechanisms, including adaptation, binding, and exclusion. Take the binding mechanism, for instance (it reminds me of the concept of connectedness in graph theory). "The idea is that social ties can bind nodes together in such a way as to construct a new entity whose properties can be different from those of its constituent elements. Binding is one of the mechanisms behind the popular notion of the performance benefits of 'structural holes'. Given an ego-network (the set of nodes with direct ties to a focal node, called 'ego,' together with the set of ties among members of the ego network), a structural hole is the absence of a tie among a pair of nodes in the ego network. A well-established proposition in social network analysis is that egos with lots of structural holes are better performers in certain competitive settings. The lack of structural holes around a node means that the node's contacts are 'bound' together—they can communicate and coordinate so as to act as one, creating a formidable 'other' to negotiate with. This is the basic principle behind the benefits of worker's unions and political alliances. In contrast, a node with many structural holes can play unconnected nodes against each other, dividing and conquering."

Is there a divide-and-conquer strategy for evidence-based medicine? Network analysis has implications for evidence-based medicine and other efforts to make change happen by disseminating knowledge: Should we be finding the structural holes in the network and distributing the evidence there?

[Fig 1. From Science, Network Analysis in the Social Sciences.]

By the way, the article begins with a nice historical recap that includes Jacob Moreno's 1932 'sociometry' study of girls running away from school (see above).

fMRI is under attack: Evidence shows advocates are guilty of the dreaded double-dipping.

Lately, 60 Minutes and other news outlets have been doing stories about fMRI (functional magnetic resonance imaging) brain scans -- saying things like "Ooh, scary. Can they use this to figure out what we're thinking?" Lesley Stahl's story on Reading Your Mind says "Neuroscience has learned much about the brain's activity and its link to certain thoughts.... [I]t may now be possible, on a basic level, to read a person's mind."

At least NPR is doing a good job of cutting through the hype. Their recent story, False Signals Cause Misleading Brain Scans, explains that fMRI "images appear amazingly crisp and precise. But scientists say the truth behind them is a little fuzzier." fMRI detects oxygen being carried to various parts of the brain. Since more oxygen indicates more brain activity, they are using the images to pinpoint when people think different types of thoughts. But the signals are really weak, and the signal-to-noise ratios are very low. Every time your heart beats, or you move your head, or you think about something randomly, a signal can occur.

You, sir, must correct the scientific record immediately! Jon Hamilton of NPR spoke with Chris Baker with the Laboratory of Brain and Cognition at the National Institute of Mental Health. "Baker says that when you're searching an ocean of noisy data, it's possible to find patterns produced purely by chance. 'There are situations in which you could apparently produce a signal out of just noise,' he says."

Baker is a co-author of the article Circular analysis in systems neuroscience: the dangers of double dipping in the journal Nature Neuroscience. Another researcher, Hal Pashler, a professor at the University of California, San Diego, is also questioning fMRI findings: He's co-authored the article Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition that appears in the journal Perspectives on Psychological Science -- the abstract uses relatively plain English to describe why they believe many published fMRI studies are flawed (see abstract at end of this post). (Pashler's web site says the article was formerly titled "Voodoo Correlations in Social Neuroscience".)

Double-dipping? NPR's Hamilton explains that "Pashler says he and his co-authors wrote the paper because they were seeing results from fMRI studies that seemed just too good to be true. The team began to ask why and zeroed in on a statistical practice sometimes called 'double dipping,' a kind of circular reasoning. Pashler and Baker say many fMRI studies do something a lot like this when they dip into the same data twice: once to pick out which parts of the brain are responding, and again to measure how strong the response is. The scientists say that's probably made a lot of results appear stronger than they really are."

Legal implications? The Neuroethics and Law blog has a discussion of Pashler's work, and references the recent Wall Street Journal article The Brain, Your Honor, Will Take the Witness Stand, subtitled "Researchers Probe How the Mind Determines Crime and Punishment, but the Science Isn't Beyond a Reasonable Doubt". Neuroscientists have used fMRI to understand how the brain might function in judicial and jury decisions: "The fMRI monitored the blood flow and oxygen demand associated with neural activity as each subject made two distinct legal judgments about blame and punishment in 50 hypothetical scenarios ranging from simple theft of a music CD to rape and murder." But if scientists' interpretations of fMRI results are statistically flawed, then maybe these scans are scary after all -- just not in the mass-media, Lesley Stahl sort of way.

Research implications? Recent emphasis on fMRI might be leading to misplaced priorities. NPR's Jon Hamilton points out "Funding agencies and other researchers are also influenced. That could mean some areas of the brain are getting attention they don't deserve, while other potentially important areas are being ignored."

Abstract from Vul, Winkielman, and Pashler's Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition (in press). Perspectives on Psychological Science.

"The newly emerging field of Social Neuroscience has drawn much attention in recent years, with high-profile studies frequently reporting extremely high (e.g., >.8) correlations between behavioral and self-report measures of personality or emotion and measures of brain activation obtained using fMRI. We show that these correlations often exceed what is statistically possible assuming the (evidently rather limited) reliability of both fMRI and personality/emotion measures. The implausibly high correlations are all the more puzzling because social-neuroscience method sections rarely contain sufficient detail to ascertain how these correlations were obtained. We surveyed authors of 54 articles that reported findings of this kind to determine the details of their analyses. More than half acknowledged using a strategy that computes separate correlations for individual voxels, and reports means of just the subset of voxels exceeding chosen thresholds. We show how this non-independent analysis grossly inflates correlations, while yielding reassuring-looking scattergrams. This analysis technique was used to obtain the vast majority of the implausibly high correlations in our survey sample. In addition, we argue that other analysis problems likely created entirely spurious correlations in some cases. We outline how the data from these studies could be reanalyzed with unbiased methods to provide the field with accurate estimates of the correlations in question. We urge authors to perform such reanalyses and to correct the scientific record."

Give your evidence more impact: Yale's primer on how to disseminate research findings.

It's such a waste when research doesn't reach an audience. Yale University has developed some good resources to help people disseminate their findings. This AIDS research program, the Center for Interdisciplinary Research on AIDS (CIRA), has developed guidelines that are now being tailored to other research groups. One of those is CARE: Community Alliance for Research and Engagement, offering a guide for disseminating community-based research. The document Beyond Scientific Publication: Strategies for Disseminating Research Findings presents recommendations that should be useful to lots of other researchers who haven't previously been involved in pubic relations, marketing, or promotion of their findings (22-page pdf).

It's not just about PR, it's an ethical obligation. The document opens by saying "Often a neglected afterthought in busy research schedules, the dissemination of key findings upon project completion is a crucial step in community-based research. In fact, we believe that researchers have an ethical obligation to ensure that research findings are disseminated to research participants, as well as other individuals and institutions in the communities in which we work. In an effort to increase ease and efficiency, this document provides key strategies for dissemination, including practical advice and specific templates you can adapt for your use. Through this strategic dissemination approach, CARE intends to distribute salient findings to affected communities, participant agencies, health departments, researchers, policy makers, and health advocacy groups." Topics include media coverage, press releases, research summaries & briefs, posters, newsletters, community publications, web sites, list-serves, events, and conferences.

Writing tips. I've done *lots* of writing that translated research into plain English. The Yale document offers some good tips to researchers, which is good, although I'm convinced many will need more guidance and hands-on help than this -- often they need a seasoned presenter to help them package up their findings. Recommendations include producing content that is:

• "Responsive: Consider your target audiences when deciding on document type.
• Concise: Make it short and to the point; be sure that information is easy to find.
• Interesting: Sort through all findings, and present just those that are new and/or compelling.
• Highlight key points: Use bulleted lists, with one finding or conclusion per bullet.
• Logical: Make sure the points progress in a logical order.
• Useful: Write clear conclusions and recommendations; if readers know what to do with the information, they will be more likely to apply it."

The document also helps authors categorize and summarize their findings so journalists, physicians, and other audiences know how it applies. All this would be even more valuable with better publishing tools (explicit metadata, etc.), but it's a good start. Here's an example of the provided templates:

EPA says greenhouse gases are hazardous to human health. How are they showing us the evidence?

The U.S. Environmental Protection Agency (EPA) has proposed a finding that greenhouse gases contribute to air pollution that may endanger public health or welfare. The agency's press release says "EPA’s proposed endangerment finding is based on rigorous, peer-reviewed scientific analysis of six gases – carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulfur hexafluoride – that have been the subject of intensive analysis by scientists around the world.

"The science clearly shows that concentrations of these gases are at unprecedented levels as a result of human emissions, and these high levels are very likely the cause of the increase in average temperatures and other changes in our climate. The scientific analysis also confirms that climate change impacts human health in several ways."

How does EPA present evidence? I'm interested in how the agency presents evidence on this complex issue, because it can significantly impact people's ability to interpret it and respond. Specifics appear in Proposed Endangerment and Cause or Contribute Findings for Greenhouse Gases under the Clean Air Act. The proposed regulations will be published in the Federal Register in the near future, and written comments will be accepted for 60 days after that. (But don't hold your breath for federal regulations to take effect anytime soon; when I worked in air quality regulatory compliance in the '90s, controversial things moved slower than molasses.)

"Fact" sheet? First, I looked over the agency's Summary of the Science Supporting EPA’s Finding That Greenhouse Gases Threaten Public Health and Welfare (pdf here). It's pretty light, which is understandable, since it attempts in two pages to summarize key points distilled from a massive amount of research and analysis. But I believe the agency could have done better -- for example, they could have hyperlinked each statement to a more detailed information source (a task that would have been much easier if the scientists and analysts authoring EPA's source material had presented their findings in a more reader-friendly way). But what really bugs me is that EPA says "All of the points in this fact sheet come from the published scientific literature...." However, many of their statements aren't "facts" at all -- they're statements of probabilities about expected future events: For instance, the agency says "Climate change is expected to worsen regional ozone pollution, with associated risks in respiratory infection, aggravation of asthma, and premature death." and "Storm impacts are likely to be more severe, especially along the Gulf and Atlantic coasts. Heavy rainfall events are expected to increase, increasing the risk of flooding, greater runoff and erosion, and thus the potential for adverse water quality effects."

I wish they'd give us more credit, and not imply that facts and findings are the same thing -- it cheapens the whole process. There are plenty of facts to go around, but lots of the important evidence is based on scientific modeling and prediction techniques. Basically, what EPA will be doing over the next few years is managing dialogue and argumentation over public policy concerning greenhouse gas (GHG) emissions; EPA and various stakeholders will be stating claims, making controversial value judgments, and issuing recommendations. And I agree with experts on logic and decision-making that "as starting points for argumentation, facts are empirical knowledge derived from observation or experience over which there is no controversy."* A statement of probability (such as "The changes to our climate may increase the likelihood of extreme and high-impact events such as more intense hurricanes.") is also a legitimate starting point for argumentation, especially when supported with evidence and qualified with estimates about uncertainty. EPA should be more explicit in identifying what types of statements are being made.

Transparency. EPA's evidence is presented in something called a Technical Support Document (TSD) (download a 171-page pdf here). The TSD "relies most heavily on existing, and in most cases very recent, synthesis reports of climate change science and potential impacts, which have gone through their own peer-review processes including review by the U.S. Government. The information in this document has been developed and prepared in a manner that is consistent with EPA's Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility and Integrity of Information Disseminated by the Environmental Protection Agency. In addition to its reliance on existing and primarily recent synthesis reports from the peer reviewed literature, it also underwent a technical review by 12 federal climate change experts, internal EPA review, and interagency review." Kudos to EPA for listing the core references most heavily relied upon in preparing the TSD (see page 4).

Gauging confidence in the evidence, and likelihood of events. EPA does a decent job of explaining how they handle uncertainty about the evidence: "Uncertainties and confidence levels associated with the scientific conclusions and findings in this document are reported, to the extent that such information was provided in the original scientific reports upon which this document is based."  EPA includes a summary of how they treat uncertainty. First, describing confidence: "On the basis of a comprehensive reading of the literature and their expert judgment, authors have assigned a confidence level to major statements on the basis of their assessment of current knowledge, as follows:

• Very high confidence: At least 9 out of 10 chance of being correct
  
• High confidence: About 8 out of 10 chance
  
• Medium confidence: About 5 out of 10 chance" and so forth.
  

This is a good way to qualify claims being made about future environmental impacts. EPA also explicitly identifies a scheme for assessing likelihood: "Likelihood refers to a probabilistic assessment of some well defined outcome having occurred or occurring in the future, and may be based on quantitative analysis or an elicitation of expert views. When authors evaluate the likelihood of certain outcomes, the associated meanings are: Virtually certain: >99% probability of occurrence; Very likely: 90 to 99% probability; Likely: 66 to 90% probability" and so forth (TSD pages 3, 6).

More graphics, please. The TSD would be easier to absorb if EPA included graphics identifying specific causes and their presumed effects. What they've given us are sets of raw data, presented in traditional ways, accompanied by lengthy narrative. (I can't resist a quick plug for the Tiny Soapbox methodology, something I developed for displaying ideas and supporting evidence. EPA could use something like this to display the specific claims they're making, and then link to the supporting evidence.)

More links, please. The bulk of EPA's 171-page TSD consists of charts, statements of findings, and references. The bibliography itself is 11 pages long. Overall this is a traditional document, not optimized for online consumption. Only a handful of the bibliographic references include a hyperlink to the source.

Plain English, please. One of EPA's key sources, the U.S. Climate Change Science Program (CCSP), calls its reports "synthesis and assessment products" (SAPs); these deliverables are intended to "address the highest priorities for U.S. climate change research, observation and decision-support needs." The TSD provides a URL for information about the clearance and review procedures for the CCSP SAPs... alas, it's a non-working URL (TSD page 3).

FYI: If you want just a quick summary of EPA's proposal, here are links to the New York Times story, the Yahoo! news story, and the EPA press release.

*R. Rieke and M. Sillars, Argumentation and Critical Decision Making (Addison Wesley, 4th Edition, 1997), page 23. Now in its 7th Edition; available from amazon.

Once again, the evidence shows dieting success depends on counting calories.

If wishing could make it true, that low-carb, eat-all-the-bacon- you-want diet would work. Or the Diet of 1,000 Smoothies. Etc., etc. But in what is being called the most rigorous and far-reaching study of various schemes, scientists have found -- once again -- that dieting success depends on simply consuming fewer calories. As explained in the Los Angeles Times, the findings tell us "any diet that is low in calories and saturated fats and high in whole grains, fruits and vegetables -- and that an individual can stick with for a lifetime -- is a reasonable choice for people who need to lose weight."

Comparison of Weight-Loss Diets with Different Compositions of Fat, Protein, and Carbohydrates was published this week in the online New England Journal of Medicine. Time quoted the lead author, Dr. Frank Sacks, hitting the nail squarely on the head: "'We have a really simple and practical message for people: it's not so much the type of diet you eat,' says Sacks. 'It's how much you put in your mouth.'" (Weren't we all secretly hoping for a different conclusion?)

I love transparency. As research abstracts go, this one is written in relatively plain English. Under Background, they simply say that "The possible advantage for weight loss of a diet that emphasizes protein, fat, or carbohydrates has not been established, and there are few studies that extend beyond 1 year." The Methods section isn't horribly obscure, telling us they randomly assigned 811 overweight adults to one of four diets that meet guidelines for cardiovascular health. And the conclusion says only that "Reduced-calorie diets result in clinically meaningful weight loss regardless of which macronutrients they emphasize." The abstract also clearly identifies the study's registration with ClinicalTrials.gov, where NIH-funded research is identified *before* the results are in -- one (long overdue) way of keeping people honest.

The Biggest Loser is on to something. Included in the results is a finding that "Satiety, hunger, satisfaction with the diet, and attendance at group sessions were similar for all diets...." But Weight Watchers and reality TV appear to have gotten it right by getting people together: The research showed that "...attendance was strongly associated with weight loss (0.2 kg per session attended)."

On this story, journalists did a reasonable job of bringing the evidence to the people. In What's the best diet? Eating less food, Time magazine online presented some research details without going overboard. Ditto for the Los Angeles Times. And both publications actually told us where we could find the published study (NEJM, beginning Feb 26).