It seems to me that model selection is usually the problem when good evidence in the data is discarded.
Most researchers are taught statistics using linear models and linear correlation. They learn how to use them well and they look statistical significance using linear models. Linear models match only linear patterns. Everyone knows about Anscombe's quartet, but it seems that there is very strong tendency to overdo linear models.
You sometimes see scatter plot that shows clear nonlinear interesting pattern that differs significantly from the null hypothesis, but authors use linear model to get P-values.
Most researchers are taught statistics using linear models and linear correlation. They learn how to use them well and they look statistical significance using linear models. Linear models match only linear patterns. Everyone knows about Anscombe's quartet, but it seems that there is very strong tendency to overdo linear models.
You sometimes see scatter plot that shows clear nonlinear interesting pattern that differs significantly from the null hypothesis, but authors use linear model to get P-values.