Autocorrelation describes sample or population observations or elements that are related to each other across time, space, or other dimensions. Correlated observations are common but problematic, largely because they violate a basic statistical assumption about many samples: independence across elements. Conventional tests of statistical significance assume simple random sampling, in which not only each element has an equal chance of selection but also each combination of elements has an equal chance of selection; autocorrelation violates this assumption. This entry describes common sources of autocorrelation, the problems it can cause, and selected diagnostics and solutions.


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