Non-Sampling Error
Candlefocus EditorSystematic non-sampling errors are very serious and can cause a study, survey or census to be unusable. Examples of systematic non-sampling errors include measuring instruments that are not functioning accurately, interviewer bias due to misinterpretation of questions or lack of knowledge, selection bias due to changing survey design during the course of data collection, and coding errors which occur when an incorrect value is assigned to a response or when data is not entered accurately. These errors occur in a consistent manner throughout the data set and can significantly increase the rate of bias in a study, survey or census.
Random non-sampling errors are errors which occur inconsistently throughout the data set and can be caused by factors such as interviewer attitude, non-response, question wording, and difficulty in following instructions. These types of errors are more difficult to detect since they are not systematic and occur in different parts of the data set. Nevertheless, they can still bias the results of a study or survey and should be looked for.
Non-sampling errors can be difficult to detect and can compromise the integrity of the data collected leading to unreliable or incorrect results. To reduce the likelihood of these errors occurring, researchers should carefully design a survey or study to ensure accuracy and consistency in the data collection process. Additionally, special effort should be made to ensure questionnaires are completed correctly, data is collected confidentially, and all entries are up to date before being recorded. By taking these measures, researchers can help reduce the occurrence of non-sampling errors and therefore, the amount of bias that is present in the survey or study.