"In other words, there is no reason to believe that the trivial variations in mortality risk observed across an enormous weight range actually have anything to do with weight or that intentional weight gain or loss would affect that risk in a predictable way."

Our Absurd Fear of Fat - NYTimes.com

"

But here’s the thing: when the economists were shown both the graph and the detailed numbers, the number of economists getting the answer spectacularly wrong — the number giving an answer of less than 10 — soared. Just working with their eyeballs, 3% of economists got it wrong. Working with the numbers as well, that proportion rose to 61%! And when a third group was given the numbers and no chart at all, fully 72% of them — professional economists all — got the answer badly wrong.

I’m certainly guilty of this kind of thing: I see a paper demonstrating a statistically significant correlation between one variable and another, and I generally assume that if the experiment were repeated, we’d see the same thing again. But that’s not actually true.

And so it’s easy to see, I think, how economists become convinced of things that the rest of us aren’t sure of at all — and how the economists often end up being wrong, while the rest of us were right to be dubious.

What’s more, if economists are bad at this kind of thing, just imagine what other social scientists are like, or even doctors. Next time you see a piece of pop-science talking about interesting findings from some paper or other, bear this in mind. A lot of papers are written; a few of them have interesting findings. Those are the papers which tend to get publicity. But there’s also a very good chance that they don’t actually show what the headlines say that they show.

"

How economists get tripped up by statistics | Felix Salmon

"Congress raises money from contributors who support and oppose the laws they make. SOPAtrack analyzes these contributions to see how often Congress votes with the money." — via @lessig

the top 10 voters with the money (meaning, they vote in line with their campaign donors) are 9 Republicans and 1 independent. the top 10 voters against the money are all Democrats. I have to admit being amazed by this, because I know the corrupting influence of money in politics is very much non-partisan. I’m not sure what to make of this yet.

ronabeinteractive:


Really really cool interactive infographic about Hollywood film budgets and profits just amazing

ronabeinteractive:

Really really cool interactive infographic about Hollywood film budgets and profits just amazing

(via sunfoundation)

dfkoz:

What am I looking at?
This map shows the breadth of vocabulary of each of the nation’s 435 voting Representatives. A darker green color indicates that a Rep has a larger vocabulary. There are 6 non-voting Representatives excluded from the map, though they are included in the rankings. The map is colored by each Rep’s “SQPD Ranking” (more details below), which measures a Representative’s usage of 3,393 different words that might be found on the SAT.

read more

"Thomas Jefferson and George Washington recorded daily weather observations, but they didn’t record them hourly or by the minute. Not only did they have other things to do, such data didn’t seem useful. Even after the invention of the telegraph enabled the centralization of weather data, the 150 volunteers who received weather instruments from the Smithsonian Institution in 1849 still reported only once a day. Now there is a literally immeasurable, continuous stream of climate data from satellites circling the earth, buoys bobbing in the ocean, and Wi-Fi-enabled sensors in the rain forest. We are measuring temperatures, rainfall, wind speeds, C02 levels, and pressure pulses of solar wind. All this data and much, much more became worth recording once we could record it, once we could process it with computers, and once we could connect the data streams and the data processors with a network. How will we ever make sense of scientific topics that are too big to know? The short answer: by transforming what it means to know something scientifically."

To Know, but Not Understand: David Weinberger on Science and Big Data - David Weinberger - Technology - The Atlantic (via wildcat2030)

(via infoneer-pulse)

"Eventually consistent semantics provide almost no guarantees regarding the recency of data returned (unbounded staleness of versions). Despite these weak guarantees, many data store users opt for eventual consistency in practice—why? It’s often faster to contact fewer replicas, and it’s also more available. However, instead of relying on anecdotal evidence, we should quantify why eventual consistency is “good enough” for many users. We can predict the expected consistency of an eventually consistent data store using models we’ve developed, called Probabilistically Bounded Staleness. It turns out that, in practice, and in the average case, eventually consistent data stores often deliver consistent data. Using PBS predictions, we can optimize the trade-off between latency and consistency and better understand why so many data store users choose eventual consistency."

Probabilistically Bounded Staleness

“How eventual is eventual consistency? How consistent is eventual consistency? PBS provides answers to these questions using new techniques and simple modeling. Find out how and play with models in your browser on this page.”

nice HTML5-based adjustable graph with a bunch of knobs for things like tolerable staleness, accuracy, replica configuration, etc. tweak away!

(via cleverhacks)

(via cleverhacks)

"It’s my view that if you put the best scientists, science communicators, and science journalists in a room, it wouldn’t take long for them to agree on the basics of good medical science reporting.

A checklist would look something like the following. Every story on new research should include the sample size and highlight where it may be too small to draw general conclusions. Any increase in risk should be reported in absolute terms as well as percentages: For example, a “50 percent increase” in risk or a “doubling” of risk could merely mean an increase from 1 in 1,000 to 1.5 or 2 in 1,000. A story about medical research should provide a realistic time frame for the work’s translation into a treatment or cure. It should emphasize what stage findings are at: If it is a small study in mice, it is just the beginning; if it’s a huge clinical trial involving thousands of people, it is more significant. Stories about shocking findings should include the wider context: The first study to find something unusual is inevitably very preliminary; the 50th study to show the same thing may be justifiably alarming. Articles should mention where the story has come from: a conference lecture, an interview with a scientist, or a study in a peer-reviewed journal, for example."

— Fiona Fox, Slate. What If There Were Rules for Science Journalism? (via futurejournalismproject)

(Source: futurejournalismproject)

sunfoundation:

Visualizing Everything Facebook Knows about You

A couple of months ago, 24-year-old Austrian law student Max Schrems  requested Facebook for all his personal data. The European arm of  Facebook, based in Dublin, Ireland, was obliged to turn over this  information, as they had to follow an European law that requires any  entity to provide full access to data about an individual, should this  individual personally request for it. Accordingly, Max received a CD  containing about 1,222 pages (PDF files), including chats he had deleted  more than a year ago, “pokes” dating back to 2008, invitations, and  hundreds of other details.

sunfoundation:

A couple of months ago, 24-year-old Austrian law student Max Schrems requested Facebook for all his personal data. The European arm of Facebook, based in Dublin, Ireland, was obliged to turn over this information, as they had to follow an European law that requires any entity to provide full access to data about an individual, should this individual personally request for it. Accordingly, Max received a CD containing about 1,222 pages (PDF files), including chats he had deleted more than a year ago, “pokes” dating back to 2008, invitations, and hundreds of other details.

curiositycounts:

OpenBible applies sentiment analysis to the Bible, why not?    (via)

curiositycounts:

OpenBible applies sentiment analysis to the Bible, why not?    (via)

(via curiositycounts)