Ergodicity

Ergodicity

This is the landing page for everything related to the ergodicity of the process you are studying.

If a process is ergodic, it does not matter when we measure it, or for what cases (e.g., what persons) we measure it: The process always has the same distributional characteristics, meaning, it is the same in terms of mean, variance, covariances, etc.. In that case, we can learn about it just as well through the study of one individual, as by studying a sample of many individuals from the population measured at one point in time, or at a few occasions.

However, when ergodicity does not hold, such generalizations across individuals, and between an individual and the population, cannot be readily made; at the very least, it would require careful further investigation to see which similarities hold across individuals, and how this translates into patterns at the population level.

Below we have specified multiple articles that help you understand ergodicity, how it relates to stationarity and homogeneity, and what the consequences are when these do or do not uphold for the process you are interested in

Think more about ergodicity

Ergodicity, stationarity and homogeneity in psychology

Considering whether statistical properties of your process remain constant or vary over time.

Consequences of non-ergodicity

Reflecting on implications when ergodicity does not hold, which is likely to be the case.

[Intra-individual versus inter-individual variation]
[The ‘within/between’ problem]