Biased vs Unbiased: Debunking Statistical Myths

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Uma reflexão sobre os enviesamentos que usamos na ciência de dados.

Anyone who attended statistical training at the college level has been taught the four rules that you should always abide by, when developing statistical models and predictions:

  1. You should only use unbiased estimates
  2. You should use estimates that have minimum variance
  3. In any optimization problem (for instance to compute an estimate from a maximum likelihood function, or to detect the best, most predictive subset of variables), you should always shoot for a global optimum, not a local one.
  4. And if you violate any of the above three rules, at least you need to make sure that your estimate, when the number of observations is large, satisfies them.

As a data scientist and ex-statistician, I violate these rules (especially #1 – #3) almost daily. Indeed, that’s part of what makes data science different from statistical science.

 

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