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The Decline Effect: why most science is bogus

January 19 2011 at 12:24 PM

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this should be a major topic of discussion with cryonicists, who are purportedly engaged in an endeavour that depends on good science, but of course it is not.
A telling quote from this excellent article: "Our beliefs are a form of blindness."

http://www.newyorker.com/reporting/2010/12/13/101213fa_fact_lehrer?currentPa


This is part of what I have been talking about here on cryonet and cryonics chat.


    
This message has been edited by CF_Moderator on Jan 19, 2011 1:35 PM


 
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A better interpretation

January 19 2011, 1:39 PM 

From Pharyngula's P.Z. Myers:

http://scienceblogs.com/pharyngula/2010/12/science_is_not_dead.php

Excerpt:

I was unimpressed with the overselling of the flaws in the science, but actually quite impressed with the article as an example of psychological manipulation.

The problem described is straightforward: many statistical results from scientific studies that showed great significance early in the analysis are less and less robust in later studies. For instance, a pharmaceutical company may release a new drug with great fanfare that showed extremely promising results in clinical trials, and then later, when numbers from its use in the general public trickle back, shows much smaller effects. Or a scientific observation of mate choice in swallows may first show a clear preference for symmetry, but as time passes and more species are examined or the same species is re-examined, the effect seems to fade.

This isn't surprising at all. It's what we expect, and there are many very good reasons for the shift.

* Regression to the mean: As the number of data points increases, we expect the average values to regress to the true meanand since often the initial work is done on the basis of promising early results, we expect more data to even out a fortuitously significant early outcome.

* The file drawer effect: Results that are not significant are hard to publish, and end up stashed away in a cabinet. However, as a result becomes established, contrary results become more interesting and publishable.

* Investigator bias: It's difficult to maintain scientific dispassion. We'd all love to see our hypotheses validated, so we tend to consciously or unconsciously select reseults that favor our views.

* Commercial bias: Drug companies want to make money. They can make money off a placebo if there is some statistical support for it; there is certainly a bias towards exploiting statistical outliers for profit.

* Population variance: Success in a well-defined subset of the population may lead to a bit of creep: if the drug helps this group with well-defined symptoms, maybe we should try it on this other group with marginal symptoms. And it doesn'tbut those numbers will still be used in estimating its overall efficacy.

* Simple chance: This is a hard one to get across to people, I've found. But if something is significant at the p=0.05 level, that still means that 1 in 20 experiments with a completely useless drug will still exhibit a significant effect.

* Statistical fishing: I hate this one, and I see it all the time. The planned experiment revealed no significant results, so the data is pored over and any significant correlation is seized upon and published as if it was intended. See previous explanation. If the data set is complex enough, you'll always find a correlation somewhere, purely by chance.

Here's the thing about Lehrer's article: he's a smart guy, he knows this stuff. He touches on every single one of these explanations, and then some. In fact, the structure of the article is that it is a whole series of explanations of those sorts. Here's phenomenon 1, and here's explanation 1 for that result. But here's phenomenon 2, and explanation 1 doesn't workbut here's explanation 2. But now look at phenomenon 3! Explanation 2 doesn't fit! Oh, but here's explanation 3. And on and on. It's all right there, and Lehrer has explained it.

But that's where the psychological dimension comes into play. Look at the loaded language in the article: scientists are "disturbed," "depressed," and "troubled." The issues are presented as a crisis for all of science; the titles (which I hope were picked by an editor, not Lehrer) emphasize that science isn't working, when nothing in the article backs that up. The conclusion goes from a reasonable suggestion to complete bullshit.

 
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Bad news for liberalism

January 20 2011, 1:55 PM 

We make radical changes in social policy based on even flimsier evidence from the social sciences. Often we get perverse, unintended results instead. For example, the welfare state, deindustrialization and the servility economy have made lower-status males even more expendable than before. No wonder many developed countries have generated hikikomori. In the U.S. some of them consume conspiracy propaganda, buy guns and go on spree killings, like the one in Tucson the other day.

BTW, I suspect the current popularity of the zombie apocalypse genre derives from this trend. Notice how often they show that society's dominant males in government and the armed forces can't control the zombie outbreak, get killed in the process and allow lower-status males the opportunity to step into the vacuum they left behind. That might sound like a step up in life over a job of gathering shopping carts in a Wal-Mart parking lot.

 
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Why Almost Everything You Hear About Medicine Is Wrong

January 24 2011, 7:58 AM 

http://www.newsweek.com/2011/01/23/why-almost-everything-you-hear-about-medicine-is-wrong.html

 
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