Biased vs Unbiased: Debunking Statistical Myths

As long as the bias is not too strong, you are better off with a robust, outlier-insensitive estimate, than with an unbiased one. It would be interesting to do some analysis, to figure out the impact that a 10% bias has on your yield metric (measured as correctness of predictions, or revenue). The impact might be much smaller than 10%. Your model might be a bad model. It is better to reduce the variance generated by your model, rather than picking up a kosher (perfect) statistical estimate.