Methods that get used

I have a conjecture regarding statistical methods:

The probability of a method being used drops by at least a factor of 2 for every parameter that has to be determined by trial-and-error.

A method could have a dozen inputs, and if they’re all intuitively meaningful, it might be adopted. But if there is even one parameter that requires trial-and-error fiddling to set, the probability of use drops sharply. As the number of non-intuitive parameters increases, the probability of anyone other than the method’s author using the method rapidly drops to zero.

John Tukey said that the practical power of a statistical test is its statistical power times the probability that someone will use it. Therefore practical power decreases exponentially with the number of non-intuitive parameters.

Related post: Software that gets used

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7 comments on “Methods that get used
  1. This is a good point. Additive models come to mind as an example. A potential counter-example is a histogram (and similar non-parametric density estimators). It takes a bit of trial and error to set the bin width, but researchers use histograms all the time.

  2. I would say that bin count or kernel bandwidth in density estimators is a fairly intuitive parameter, which is why it doesn’t impede adoption.

  3. DavidC says:

    Maybe worth noting that sometimes a parameter can become not-a-parameter. If the method is “choose the L2 penalty term by cross validation”, then that term is no longer an input parameter.

  4. TomF says:

    A corollary is that whatever method you learned in school is likely to seem more intuitive than something you learn later — even if both have the same number of parameters.

  5. Adam Rosien says:

    The same goes for programming languages and functions. A function has three boolean parameters, which is which?

    Strong typing helps, especially when you enforce a rule, somehow, that functions should have more than one argument of each type. Then functions are resistant to parameter reordering.

  6. I think DavidC is right. A method that has a non-intuitive parameter demands another method for selecting that parameter.

    This conjecture also makes me think about Bret Victor’s Ladder of Abstraction ( which gives lots of ways to experience these parameters.

  7. Edvin says:

    On the other hand, parameters to be determined are parameters that can be tweaked to fit your model predictions to your expectations. This sometimes helps their popularity.

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