Funny anecdote: where I work, one of our departments had a model we used to predict the working characteristics of our most popular product. Over time, we noticed that real-world data deviated from our calculations, so the model was regularly adjusted to bring the predictions in line.<p>Recently we had to model a new system, and none of our numbers looked remotely right. Looking at the code, the whole program had essentially been turned into a lookup table that was completely useless at actually &#x27;predicting&#x27; anything we hadn&#x27;t seen before. Since then, the system&#x27;s been completely overhauled, and now it much more accurately predicts general characteristics even if it appears less precise than before.<p>My point&#x27;s kind of gotten away from me, but I guess it&#x27;s just easy to see how people can fall into the trap of leaning on perceived reliability and using it to avoid making radical changes.