Bestimment

Bestimment is a term used in informal or emerging discussions to denote the act or result of producing a “best” estimate for a parameter or quantity. It is a neologism formed by combining best and estimate and is not widely standardized in statistical literature. In practice, bestimment may refer to a point estimate selected according to a particular criterion, or to the reported single value chosen to summarize uncertainty for decision-making.

Etymology and usage are informal. The word is often introduced in online forums, data-science blogs, or exploratory writing where authors seek a concise label for a preferred single-number summary. Because there is no formal definition, the exact method behind a bestimment can vary between authors or disciplines.

Construction methods vary with context. A bestimment might be the posterior mean or posterior mode in Bayesian analysis, the maximum likelihood estimate in frequentist settings, or another point estimate that minimizes a chosen loss function. Some practitioners use bestimment to refer to a decision-optimized value selected under a specified criterion, rather than a universally defined statistical quantity.

Limitations and reception. The lack of a precise, universally accepted definition can hinder clear communication in rigorous work. Where clarity is required, standard terms such as point estimate, maximum likelihood estimate, Bayesian estimator, or posterior mean are typically preferred.

See also: estimation, point estimate, maximum likelihood estimation, Bayesian inference, posterior mean, posterior mode, decision theory.