The Mysterious Decline Effect | Wired Science | Wired.com - 0 views
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Question #1: Does this mean I don’t have to believe in climate change? Me: I’m afraid not. One of the sad ironies of scientific denialism is that we tend to be skeptical of precisely the wrong kind of scientific claims. In poll after poll, Americans have dismissed two of the most robust and widely tested theories of modern science: evolution by natural selection and climate change. These are theories that have been verified in thousands of different ways by thousands of different scientists working in many different fields. (This doesn’t mean, of course, that such theories won’t change or get modified – the strength of science is that nothing is settled.) Instead of wasting public debate on creationism or the rhetoric of Senator Inhofe, I wish we’d spend more time considering the value of spinal fusion surgery, or second generation antipsychotics, or the verity of the latest gene association study. The larger point is that we need to be a better job of considering the context behind every claim. In 1952, the Harvard philosopher Willard Von Orman published “The Two Dogmas of Empiricism.” In the essay, Quine compared the truths of science to a spider’s web, in which the strength of the lattice depends upon its interconnectedness. (Quine: “The unit of empirical significance is the whole of science.”) One of the implications of Quine’s paper is that, when evaluating the power of a given study, we need to also consider the other studies and untested assumptions that it depends upon. Don’t just fixate on the effect size – look at the web. Unfortunately for the denialists, climate change and natural selection have very sturdy webs.
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biases are not fraud. We sometimes forget that science is a human pursuit, mingled with all of our flaws and failings. (Perhaps that explains why an episode like Climategate gets so much attention.) If there’s a single theme that runs through the article it’s that finding the truth is really hard. It’s hard because reality is complicated, shaped by a surreal excess of variables. But it’s also hard because scientists aren’t robots: the act of observation is simultaneously an act of interpretation.
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(As Paul Simon sang, “A man sees what he wants to see and disregards the rest.”) Most of the time, these distortions are unconscious – we don’t know even we are misperceiving the data. However, even when the distortion is intentional it’s still rarely rises to the level of outright fraud. Consider the story of Mike Rossner. He’s executive director of the Rockefeller University Press, and helps oversee several scientific publications, including The Journal of Cell Biology. In 2002, while trying to format a scientific image in Photoshop that was going to appear in one of the journals, Rossner noticed that the background of the image contained distinct intensities of pixels. “That’s a hallmark of image manipulation,” Rossner told me. “It means the scientist has gone in and deliberately changed what the data looks like. What’s disturbing is just how easy this is to do.” This led Rossner and his colleagues to begin analyzing every image in every accepted paper. They soon discovered that approximately 25 percent of all papers contained at least one “inappropriately manipulated” picture. Interestingly, the vast, vast majority of these manipulations (~99 percent) didn’t affect the interpretation of the results. Instead, the scientists seemed to be photoshopping the pictures for aesthetic reasons: perhaps a line on a gel was erased, or a background blur was deleted, or the contrast was exaggerated. In other words, they wanted to publish pretty images. That’s a perfectly understandable desire, but it gets problematic when that same basic instinct – we want our data to be neat, our pictures to be clean, our charts to be clear – is transposed across the entire scientific process.
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