Research often starts with asking a question and gathering data related to that question. Once we have data, what can we say about the parameters of a general population? This is the question Bradley Efron tried to answer as he formulated the bootstrap method, which is a resampling method that gives confidence intervals of parameters from a population. While this method can be used algebraically with no simulation, its most common use includes simulation as this leverages the power of the computer, a tool which was just becoming readily available in Efron’s time. The bootstrap method is quite useful, but there are situations where it does not provide satisfactory confidence intervals. Efron set out to mitigate these inconsistencies by creating the bias-corrected bootstrap method. This talk will address the development of the bootstrap methods and also look to the future at further improvements that could be made.