I have a dataset of bimodal population. It contains a smaller peak, which is considered to be "bad", and a bigger peak. I try to separate the bad part of data from the rest of data. What I did was: first I did a kernel density estimation, then found the local maximum of this small peak, and the local minimum of the pit between two peaks, then I took the midpoint (arithmetic mean of x-coordinates) of them, and define it as a cutoff. Everything below this cutoff is considered "bad". The reason I took midpoint instead of pit is because I tried to be more conservative.
Now I would like to ask: Is what I did reasonable? If yes, how can I explain my action in a statisticians-favored manner? If not, how can I change? (Any other methods are welcome, especially those implemented in R.) Thank you!
Here's the figure.