As your data set grows, unbounded variance grows nonlinearly compared to the valid data. As variance increases, deviations grow larger, and happen more frequently. This causes spurious relationships grow much faster than authentic ones. The noise becomes the signal.
Overfitting happens when you try add too many variables to your training data. This happens because people think that by adding more data (variables), they can remove bias. What they end up doing, is becoming better at describing the data they have, but not the overall phenomena.
As your data set grows, unbounded variance grows nonlinearly compared to the valid data. As variance increases, deviations grow larger, and happen more frequently. This causes spurious relationships grow much faster than authentic ones. The noise becomes the signal.
Related: Overfitting: https://en.wikipedia.org/wiki/Overfitting
Overfitting happens when you try add too many variables to your training data. This happens because people think that by adding more data (variables), they can remove bias. What they end up doing, is becoming better at describing the data they have, but not the overall phenomena.
It's counter intuitive but mathematically true.