theorem this curve is relatively flat most of the way across and starts to curve down gently as the samples become more realistic and then finally the maximum likelihood cost which has the negation of an exponential function in it is very flat on the left side but then shoots off exponentially downward as we get very far to the right so we can see that we would actually incur very high variance in the estimate of the gradient if we were to use that particular function because almost all the gradient comes from a single member