how a non sampling error works
non sampling errors may be present in both samples and censuses in which an entire population is surveyed non sampling errors fall under two categories random and systematic random errors are believed to offset each other and therefore most often are of little concern systematic errors on the other hand affect the entire sample and therefore present a more significant issue random errors generally will not result in scrapping a sample or a census whereas a systematic error will most likely render the data collected unusable non sampling errors are caused by external factors rather than an issue within a survey study or census there are many ways non sampling errors can occur for example non sampling errors can include but are not limited to data entry errors biased survey questions biased processing decision making non responses inappropriate analysis conclusions and false information provided by respondents special considerationswhile increasing sample size can help minimize sampling errors it will not have any effect on reducing non sampling errors this is because non sampling errors are often difficult to detect and it is virtually impossible to eliminate them non sampling errors include non response errors coverage errors interview errors and processing errors a coverage error would occur for example if a person were counted twice in a survey or their answers were duplicated on the survey if an interviewer is biased in their sampling the non sampling error would be considered an interviewer error in addition it is difficult to prove that respondents in a survey are providing false information either by mistake or on purpose either way misinformation provided by respondents count as non sampling errors and they are described as response errors technical errors exist in a different category if there are any data related entries such as coding collection entry or editing they are considered processing errors 