KnowraIndependent and identically distributed random variablesLinked fromLinked fromThe 20 pages that link to Independent and identically distributed random variables, each with the reason it gives.All 20Broader topic 1Related 13Narrower topic 4Compared with 2Maximum likelihood estimationRelated: This common sampling assumption makes the joint likelihood a product of individual contributions.Law of large numbersRelated: This common setup supplies repeated observations with a shared expectation for classic versions of the theorem.Likelihood functionRelated: Under this common assumption, the sample likelihood factors into a product of individual observation probabilities or densities.Random walkRelated: Many basic walks are sums of repeated steps with this common independence structure.Cross-validationRelated: Ordinary random folds assume observations are exchangeable under a shared data-generating process.Likelihood ratioRelated: Under this assumption, the likelihood ratio for a sample factors into observation-level ratios.Strong law of large numbersRelated: This standard setting supplies a familiar version of the theorem's assumptions.Bootstrap methodRelated: The ordinary bootstrap most directly fits observations modeled as independent draws from one population.Degrees of freedomRelated: Many standard degrees-of-freedom formulas assume observations with this structure.Student's t-distributionRelated: Independence and a common normal distribution support the exact classical t result.Coupon collector's problemRelated: Each draw is modeled as an independent choice from the same uniform distribution.Glivenko–Cantelli theoremRelated: The classical theorem assumes observations drawn under this common sampling model.Sample mean and covarianceRelated: This common sampling model yields standard unbiasedness and consistency results for the estimates.