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Who is missing from your sample?

A large sample can still give a distorted picture if the selection process excludes people.

Sampling bias occurs when the way a sample is collected systematically favours some parts of the population over others in a way that distorts the result. Asking only the easiest people to reach can create such a problem.

Increasing the sample size does not necessarily repair the selection method. Ten thousand responses from the same narrow group may still leave important experiences out. Begin by naming the population your conclusion is about, then examine who had a realistic chance to be included.

A small example

A survey about library opening hours is handed only to visitors on weekday mornings. It may miss people who cannot visit at those times.

Keep in mind

Unequal selection probabilities can be handled in designed surveys; the issue is whether the method supports the intended inference.

Source & attribution

Introductory Statistics 2e — Data, Sampling, and Variation in Data and Sampling

OpenStax contributors · OpenStax, Rice University

CC BY 4.0. Rewritten as a self-contained explanation; the example and reflection are Vakataka additions. No source images reproduced.

Source edition: 2024-12-16T15:08:45Z

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