摘要： Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.
There are infinite ways to incorrectly reason from data, some of which are much more obvious than others. Given that people have been making these mistakes for so long, many statistical fallacies have been identified and can be explained. The good thing is that once they are identified and studied, they can be avoided. Let's have a look at a few of these more common fallacies and see how we can avoid them.
1. Cherry Picking
...The idea of cherry picking is a simple one, and something you have definitely done before: the intentional selection of data points which help support your hypothesis, at the expense of other data points which either do not support your hypothesis or actively oppose it.
2. McNamara Fallacy
...Putting the statistical blinders on and placing all of your trust in a single, simple metric is not going to paint a full picture of whatever it is that you are doing.
3. Cobra Effect
The Cobra Effect is an unintended consequence from what was thought to be a solution to a problem, but which instead makes the problem worse....
4. Simpson's Paradox
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5. Data Dredging
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Full Text： kdnuggets
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