With bootstrapping and bagging, we resample from the dataset and end up building a model or estimating some sample statistic using the sampled data, which typically consists of at least $33\%$ duplicate data.
My questions are:
(1) Why do even need to use the duplicated data? Why don't we simply discard them, and just use the unique data for each bag?
(2) When the duplicated data is used, aren't you putting more emphasis on those data during the learning process? If so, doesn't that introduce some bias into the model?