In bootstrapping, data are resampled with replacement in order to empirically estimate the sampling distribution. This method does not require the data to follow any specific distribution, which is one of its advantages over Monte Carlo simulation. Monte Carlo simulation typically assumes a known probability distribution for the data, whereas bootstrapping is a non-parametric method that does not make any assumptions about the underlying distribution. Bootstrapping is useful when the underlying distribution of the data is unknown or when the sample size is small. It creates multiple simulated samples (bootstrap samples) by resampling the original data set with replacement, and then calculates the statistic of interest for each bootstrap sample. This process generates an empirical distribution of the statistic, which can be used to estimate confidence intervals, conduct hypothesis tests, or perform other statistical analyses. The correct answer to the question is B, as it accurately describes the process of resampling with replacement in bootstrapping.