Sampling error is the difference between the sample result and the true population parameter that arises because only a subset of the population is observed.

It reflects the extent to which the sample may not perfectly represent the population due to the randomness inherent in sampling. Sampling error can be reduced by increasing the sample size, but it cannot be entirely eliminated.

Two major non-sampling errors are:

Measurement Error: This occurs when there are inaccuracies in the data collection process. It can arise from faulty measurement instruments, biased survey questions, respondent errors (such as misunderstanding questions or providing inaccurate responses), or data recording mistakes.
Nonresponse Error: This occurs when some members of the selected sample do not respond or participate in the survey. If the non-respondents differ significantly from the respondents in terms of the survey subject, it can lead to biased results. Nonresponse error can be mitigated by follow-up efforts to encourage participation or by weighting the responses to account for nonresponses.