One of the two reasons for the difference between an estimate (from a sample) and the true value of a population parameter; the other reason being the error caused because data are collected from a sample rather than the whole population (sampling error). Non-sampling errors have the potential to cause bias in surveys or samples.
There are many different types of non-sampling errors and the names used for each of them are not consistent.
Some examples of non-sampling errors are:
• The sampling process is such that a specific group is excluded or under-represented in the sample, deliberately or inadvertently. If the excluded or under-represented group is different, with respect to survey issues, then bias will occur.
• The sampling process allows individuals to select themselves. Individuals with strong opinions about the survey issues or those with substantial knowledge will tend to be over-represented, creating bias.
• If people who refuse to answer are different, with respect to survey issues, from those who respond then bias will occur. This can also happen with people who are never contacted and people who have yet to make up their mind.
• If the response rate (the proportion of the sample that takes part in a survey) is low, bias can occur because respondents may tend consistently to have views that are more extreme than those of the population in general.
• The wording of questions, the order in which they are asked and the number and type of options offered can influence survey results.
• Answers given by respondents do not always reflect their true beliefs because they may feel under social pressure not to give an unpopular or socially undesirable answer.
• Answers given by respondents may be influenced by the desire to impress an interviewer.
Curriculum achievement objectives references
Statistical investigation: Levels (7), (8)
Statistical literacy: Levels 7, (8)