This question already has an answer here:
In layman's terms, what is the difference between predicting and explaining in statistics? I was looking for the differences between AIC and BIC and found this post with an answer stating:
My quick explanation is
AIC is best for prediction as it is asymptotically equivalent to cross-validation. BIC is best for explanation as it is allows consistent estimation of the underlying data generating process.
This makes me think that in the same vein as precision and accuracy there is some core distinction here that significantly effects when and how to use a lot of statistical procedures. I googled it and only found a variety of papers including this one, but this is far too rigorous for my question given my current knowledge. Could anyone provide an intuitive exposition of the difference and perhaps some examples of how it affects the use of statistical methods and tools?