Original sentence: A once common method of imputation was hot-deck imputation where a missing value was imputed from a randomly selected similar record.Cold-deck imputation, by contrast, selects donors from another dataset.In statistics, imputation is the substitution of some value for a missing data point or a missing component of a data point.Since computer power has advanced rapidly and punched cards are no longer used, more sophisticated methods of imputation have generally superseded the original random and sorted hot deck imputation techniques, such as nearest neighbour hot deck imputation and the approximate Bayesian bootstrap.Since standard analysis techniques do not reflect the additional uncertainty due to imputing for missing data, further adjustments (such as multiple imputation or a Rao–Shao correction) are necessary to account for this.The stack of cards was "hot" because it was currently being processed.The term "hot deck" dates back to the storage of data on punched cards, and indicates that the information donors come from the same dataset as the recipients
Edited sentence: Appropriate methods for addressing the impact of missing data -- those considered valid -- are the inclusion of use of basic descriptive statistics (especially sample variance, standard error of the mean, p-value and confidence interval).Attempts to do so either cannot be justified, or an attempt is made to justify doing so using so-called "circular" logic.In other words, missing data cannot be fabricated -- or "imputed".It refers to the substitution of some value for a missing data point or a missing component of a data point.Limits to how well an investigator can recover from the impact of missing data is determined by what data is missing, how much data is missing versus how much data was collected, and the professional expertise demomstrated by the author of the report.Since these are an abstraction of raw data they are not reversible.That is, they inherently lack the factors necessary to re-produce the exact data set from which they were derived.The standard used to address missing data (for example, in medical research) is to directly and overtly address it as a source of bias.This is one of the reasons study design is critical to publishing -- the potential impact of missing data can devastate months of work and millions of dollars.This term describes the invalid and unethical fabrication of statistical data