Imputation means filling missing values in a data set.
At Statistics Denmark we collect a lot of data, some of which have missing values. If there is only one missing value in a questionnaire with 1000 questions it would be natural to impute this value, but if only one out of 1000 questions had been answered, it would probably be useless to impute the remaining values. The purpose of the data collection should also be take into consideration when imputing. If imputation is done in a good way, it leads to easier statistics and better conclusions, but it can also lead to confusion and biased results. If the probability that a response is missing depends not only on the observed data, but also on the unobserved data, the imputation algorithms here can not be used.