Many quantitative genetic statistics are functions of variance components, for which a large number of replicates is needed for precise estimates and reliable measures of uncertainty, on which sound interpretation depends. Moreover, in large experiments the deaths of some individuals can occur, so methods for analysing such data need to be robust to missing values. We show how confidence intervals for narrow-sense heritability can be calculated in a nested full-sib/half-sib breeding design (males crossed with several females) in the presence of missing values. Simulations indicate that the method provides accurate results, and that estimator uncertainty is lowest for sampling designs with many males relative to the number of females per male, and with more females per male than progenies per female. Missing data generally had little influence on estimator accuracy, thus suggesting that the overall number of observations should be increased even if this results in unbalanced data. We also suggest the use of parametrically simulated data for prior investigation of the accuracy of planned experiments. Together with the proposed confidence intervals an informed decision on the optimal sampling design is possible, which allows efficient allocation of resources.