In large-scale assessment programs such as NAEP, TIMSS and PISA, students' achievement data sets provided for secondary analysts contain so-called "plausible values." Plausible values are multiple imputations of the unobservable latent achievement for each student. In this article it has been shown how plausible values are used to: (1) address concerns with bias in the estimation of certain population parameters when point estimates of latent achievement are used to estimate those population parameters; (2) allow secondary data analysts to employ "standard" techniques and tools (e.g., SPSS, SAS procedures) to analyse achievement data that contains substantial measurement error components; and (3) facilitate the computation of standard errors of estimates when the sample design is complex. The advantages of plausible values have been illustrated by comparing the use of maximum likelihood estimates and plausible values (PV) for estimating a range of population statistics.