Statistics - Prediction and Explanation
Some types of statistical methods are focused on predicting what will happen.
Other types of statistical methods are focused on explaining how things are connected.
Some statistical methods are not focused on explaining how things are connected. Only the accuracy of prediction is important.
Many statistical methods are successful at predicting without giving insight into how things are connected.
Some types of machine learning let computers do the hard work, but the way they predict is difficult to understand. These approaches can also be vulnerable to mistakes if the circumstances change, since the how they work is less clear.
Note: Predictions about future events are called forecasts. Not all predictions are about the future.
Some predictions can be about something else that is unknown, even if it is not in the future.
Different statistical methods are often used for explaning how things are connected. These statistical methods may not make good predictions.
These statistical methods often explain only small parts of the whole situation. But, if you only want to know how a few things are connected, the rest might not matter.
If these methods accurately explains how all the relevant things are connected, they will also be good at prediction. But managing to explain every detail is often challenging.
Some times we are specifically interested in figuring out if one thing causes another. This is called causal inference.
If we are looking at complicated situations, many things are connected. To figure out what causes what, we need to untangle every way these things are connected.
Note: Making conclusions about causality should be done carefully.