If estimates are based on samples, they should be accompanied by appropriate standard errors and confidence intervals. This is true for scientific research in general, and is even more important if estimates are used to inform and evaluate policy measures such as those aimed at attaining the Europe 2020 poverty reduction target. In this article I pay explicit attention to the calculation of standard errors and confidence intervals, with an application to the European Union Statistics on Income and Living Conditions (EU-SILC). The estimation of accurate standard errors requires among others good documentation and proper sample design variables in the dataset. However, this information is not always available. Therefore, I complement the existing documentation on the sample design of EU-SILC and test the effect on estimated standard errors of various simplifying assumptions with regard to the sample design. It is shown that accounting for clustering within households is of paramount importance. Although this results in many cases in a good approximation of the standard error, taking as much as possible account of the entire sample design generally leads to more accurate estimates, even if sample design variables are partially lacking. The effect is illustrated for the official Europe 2020 indicators of poverty and social exclusion and for all European countries included in the EU-SILC 2008 dataset. The findings are not only relevant for EU-SILC users, but also for users of other surveys on income and living conditions which lack accurate sample design variables.