is uncertainty associated with them and this uncertainty can, in principle, be reflected in a standard deviation for the prevalence. Uncertainty in the prevalence estimates can come from three sources: sampling variability, variability associated with the EAR, and variability associated with collection of intake data.
Any time a sample of individuals is used to make inferences about a larger group, a statistical error (often called sampling variability) is incurred. In the case of dietary assessment, not only are the intake data obtained for just a sample of individuals in the group, but also the sample of intake days is small for each of those individuals. Therefore, two sources of sampling variability are immediately identifiable —one arising from not observing the entire population and one arising from not observing intake on all days for each individual.
Statistical techniques can be used to estimate the amount of sampling variability associated with prevalence estimates, although the computations are complex. When standard deviations can be calculated, it is appropriate to report not only the prevalence estimate but also its standard deviation. For example, for group X the prevalence of inadequate intake of nutrient Y was a percent ± b percent, where a is the estimated percent prevalence of nutrient inadequacy and b is the standard deviation of the prevalence estimate. When b is small relative to a, the prevalence has been estimated with a good degree of accuracy.
An additional consideration when determining the sampling variability is the effect of the survey design. Dietary intake data are typically collected in complex surveys, and thus the survey design must be taken into account when estimating standard deviations. Additional information on the estimation of standard deviations under complex survey designs, or in particular, about the estimation of standard deviations for prevalence estimates can be found in Nusser et al. (1996) and Wolter (1985).
Variability associated with the EAR may increase the uncertainty around prevalence estimates. Both the probability approach and the cut-point method use the EAR when estimating prevalence of inadequacy. However, the EAR is itself an estimate, and thus has its own uncertainty. Practical statistical approaches have not yet been developed for combining the two uncertainties—those around intake