The Exaggerated Promise of So-Called Unbiased Data Mining | WIRED - 1 views
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The Feynman trap—ransacking data for patterns without any preconceived idea of what one is looking for—is the Achilles heel of studies based on data mining. Finding something unusual or surprising after it has already occurred is neither unusual nor surprising. Patterns are sure to be found, and are likely to be misleading, absurd, or worse.
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A standard neuroscience experiment involves showing a volunteer in an MRI machine various images and asking questions about the images. The measurements are noisy, picking up magnetic signals from the environment and from variations in the density of fatty tissue in different parts of the brain. Sometimes they miss brain activity; sometimes they suggest activity where there is none.A Dartmouth graduate student used an MRI machine to study the brain activity of a salmon as it was shown photographs and asked questions. The most interesting thing about the study was not that a salmon was studied, but that the salmon was dead. Yep, a dead salmon purchased at a local market was put into the MRI machine, and some patterns were discovered. There were inevitably patterns—and they were invariably meaningless.
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This article relates to our discussion in class about data mining. Scientists assume that patterns in data are true instead of making a hypothesis and trying to see if their hypothesis is true. These assumptions can lead to false conclusions. Also, this article talks about how people go through all of this data without knowing what they are looking for. When someone does this, it is called The Feynman Trap. I also found it interesting how someone studied the brain activity of a dead fish and still found patterns.