There is no such thing as a perfect model.
It doesn’t exist.
There are better and worse models, and which we end up using in practice often has to do with how much time we have to devote to the task at issue, and the resources at our disposal.
It also has to do with the quality of the available information, and how important the question we are trying to answer is in the grand scheme of things.
I’m not going to train up a neural network or whip out a Monte Carlo simulation to win an argument on a video game Forum (not saying either would be used in this case, just providing examples of overkill). It doesn’t make sense, nor does it provide enough additional value to be worth the effort.
To claim that every, single point made with data has to be perfectly unbiased is to claim that almost no study done in the 21st century is valid. And it’s to say half of the field of Data Science is barking up the wrong tree.
While it’s very bad to present information with a clear bias (without identifying the bias), it’s worse to completely dismiss a point because it’s not a perfectly designed study – especially if the stakes are low, and the person presenting the information did not think that his assertions would be under Peer Review.
