# Explaination vs. Prediction

Again, some older post I had lying around. Nonetheless, the topic is still prevalent.

I recently read a great paper named To Explain or To Predict? by Galit Shmueli. She explains the differences between the “old-school” explanatory statistics and predictive statistics. I saw lots of her observations by myself.

That means predictions are often regarded as unscientific and therefore there’s a bit of a lack of good literature – lately the situation became better with the uprising of machine learning.
Nonetheless, most students don’t learn how to make predictions and you see how people use $R^2$ to validate models.

Sure, there are some departments that teach how to predict but they are still in the minority. Of course, there’s this other trend with Big Data. I’m personally not really excited by Big Data rather by data at all.