Truncation is a process that results in the omission of data that are beyond a threshold.
Truncation is a process that results in data that are beyond a threshold being omitted from a dataset. Truncation is related to, but distinct from, censoring and missing data. When data are censored, they are listed in the dataset, but their values are only partially known, e.g., we might know that a value is $>t$. On the other hand, with a truncated dataset, values $>t$ are not included in the dataset. With missing values, we know that an observation exists, but we do not know what the value is for some variable, whereas with truncation that observation is not recorded at all. Another distinction is between data that are not in our dataset, but are assumed to exist (truncation), and data that cannot exist for some reason.
An example of truncated data might be the scores for people in a program where people are not eligible for the program unless their scores are beyond some cutoff.
