Standard error is a measure of sampling error. There are others, but standard error is, by far, the most commonly used when dealing with survey data. But one important point: sampling error is NOT the only reason for a difference between your survey estimate (based on your survey sample) and the true value in the population. Another, and arguably more important, reason for this difference is bias. Bias can be introduced when designing the sampling scheme, writing the questionnaire or data collection form, collecting the survey data, or analyzing the survey data. Most forms of bias cannot be calculated nor measured after the data are collected, and are, therefore, often invisible. Bias must be avoided by using correct procedures at each step of the survey process. Bias has NOTHING to do with sample size which affects only sampling error and standard error. As a result, large sample sizes do NOT eliminate bias. In fact, the larger your sample size, the more teams you need to collect data for whom it is more difficult to provide the necessary supervision; thus, increasing the likelihood of bias in the data collection. I think it best to use a minimal sample size so that survey managers can provide good supervision and data quality checks to ensure a minimum of potentially invisible bias.