Sampling error

Sampling error is not an error in the everyday sense, and the name is what trips students up. Even a flawless simple random sample produces a statistic that misses the parameter, purely because a different random sample would have landed somewhere else. If the true population proportion is $p = 0.50$ and one random sample of 100 gives $\hat{p} = 0.46$ (p-hat, the sample proportion), the sampling error for that sample is $0.46 - 0.50 = -0.04$. Sampling error shrinks as $n$ grows, but errors from bad methods, such as undercoverage or nonresponse, are non-sampling errors and a bigger sample will not touch them.