3 ms·

The flip side is with messy real world data you just need a model that's ok enough, rather than being concerned whether the p-value is this or that.

• minimaxir 2y ago
  At that point, if you don't care about interpretable coefficients, you might as well use gradient-boosted trees or a full neural network instead.
  • borroka 2y ago
    It depends on the "severity" of the violation of assumptions--you can also use GAMs to add flexible nonlinear relationships--and the amount of data you are working with. Statistical modeling is a nuanced job.
    • minimaxir 2y ago
      I tried to argue that while at CMU and it didn't go well.
      • borroka 2y ago
        They may not know at CMU that the vast majority of applied, trained-on-data statistical models that help run the modern world seriously violate one or more of the model's assumptions.