What Is a Non-Sampling Error?
Let me explain what a non-sampling error is—it's a statistical term for an error that happens during data collection, making the data differ from the true values. You need to know that this differs from a sampling error, which is just about differences between sample values and the full universe values because you can't sample everything in a survey or census.
Key Takeaways
Remember, a non-sampling error is what we call an error in statistics that pops up during data collection, leading to data that's not matching the true values. These can be random or systematic, and they're tough to spot in surveys, samples, or censuses. Systematic ones are worse than random ones because they might force you to scrap the whole study, survey, or census. The more errors there are, the less reliable your information becomes. When these errors happen, the bias in your study or survey increases. On the other hand, sampling errors can occur even without any mistakes, just because a sample won't perfectly match the universe it's drawn from, and you can minimize that by making your sample bigger. Non-sampling errors cover everything else, including issues from poor sampling techniques.
How a Non-Sampling Error Works
Non-sampling errors can show up in both samples and full censuses where you survey the entire population. They fall into two categories: random and systematic. Random errors tend to offset each other, so they're usually not a big concern. Systematic errors, though, affect the whole sample and are a much bigger problem. Random errors won't typically make you scrap a sample or census, but systematic ones likely will, rendering the data unusable.
These non-sampling errors come from external factors, not from issues within the survey, study, or census itself. There are plenty of ways they can occur—for instance, data entry errors, biased survey questions, biased processing or decision-making, non-responses, inappropriate analysis conclusions, and false information from respondents.
Special Considerations
While you can minimize sampling errors by increasing your sample size, that won't help with non-sampling errors. These are often hard to detect, and eliminating them is practically impossible. Non-sampling errors include things like non-response errors, coverage errors, interview errors, and processing errors. For example, a coverage error happens if someone is counted twice in a survey or their answers are duplicated. If an interviewer is biased in their sampling, that's an interviewer error.
It's also tough to prove if respondents are giving false information, whether by mistake or on purpose—that counts as a response error. Technical errors are in their own category; if there are issues with data-related entries like coding, collection, entry, or editing, those are processing errors.
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