achieves_planned_objective statistical model validation boostrapping Although widely accepted that high throughput biological data are typically highly noisy, the effects that this uncertainty has upon the conclusions we draw from these data are often overlooked. However, in order to assign any degree of confidence to our conclusions, we must quantify these effects. Bootstrap resampling is one method by which this may be achieved. Bootstrapping is a data transformation process which estimates the precision of sampling statistics by drawing randomly with replacement from a set of data points Bootstrapping is a statistical method for estimating the sampling distribution of a statistic by sampling with replacement from the original data, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio, correlation coefficient or regression coefficient Helen Parkinson boostrapping cross validation objective metadata complete