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This kind of non-sampling error can be avoided by thoroughly understanding your research question before you begin constructing a questionnaire or selecting respondents. The shopper might make the purchase decision, but the children influence the cereal choice. Who to survey? It might be the entire family, the person who most often does the grocery shopping, or the children. For example, imagine a survey about breakfast cereal consumption in families. This error occurs when the researcher does not understand who they should survey.
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Population specification error (non-sampling error)
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However, sampling error can absolutely be reduced by following good practices – more on that below.ĭownload the eBook: How to Minimize Sampling and Non-Sampling Errors Sampling and non-sampling errors: 5 examples 1. In this sense, sampling error is a feature of sampling rather than a human error, and it can’t be completely avoided. Interestingly, it’s not usually possible to quantify the degree of sampling error in a study since – by definition – the relevant data for the entire population is not measured.Īs OECD explains, a population will never be perfectly represented by a sample because the population is larger and more complete. Meanwhile, sampling error means the difference between the mean values of the sample and the population, so it only happens when you’re working with representative samples. Non-sampling errors can happen whether you’re working with a representative sample (such as with a national survey) or doing total enumeration (such as when you’re carrying out employee experience surveys with your workforce.)
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Problems like choosing the wrong people, letting bias enter the picture, or failing to anticipate that participants will self-select or fail to respond: those are non-sampling errors, and we’ll cover several of the worst offenders later in the article. Somewhat confusingly, the term ‘sampling error’ doesn’t mean mistakes researchers have made when selecting or working with a sample.
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For this reason, it is important to understand both sampling error and non-sampling errors so you can prevent them from causing problems in your research.ĭownload the eBook: How to Minimize Sampling and Non-Sampling Errors Non-sampling errors vs. (Too big and you’re putting in lots of work for no meaningful gain too small and you can’t be sure your sample is representative.)īut there’s more to doing sampling well than just getting the right sample size. Perhaps the most well-known of these is getting your sample size right. To make sure that your sample is a fair representation, you need to follow some survey sampling best practices. The practical solution is to take a representative sample – a group that stands in for the whole of your research population. When you’re running a survey, you’re usually interested in a much bigger group of people than you can reach.
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(If you’re all clued up on sampling already, feel free to skip ahead to the next section.) To understand what sampling error is, you first need to know a little bit about sampling and what it means in survey research. What is sampling error and why does it matter?