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With bootstrapping, (i) the entire data set is resampled with replacement many times, (ii) on each resampling the statistics is computed, and (iii) the statistics from each resampling are combined in a special way [ 22 ] . In a recent study comparing these adjustments with other types of statistics in a different setting, Steyerberg et al [ 22 ] found that the split-sample approach tended to underestimate performance, cross-validation performed poorly on some statistics that were not normally distributed, and bootstrapping performed best overall.