Estimation

For other uses, see Estimation (disambiguation).
"Estimate" and "Estimated" redirect here. For other uses, see Estimate (disambiguation) and Estimated sign.
The exact number of candies in this jar cannot be determined by looking at it, because most of the candies are not visible. It can be estimated by assuming that the density of the unseen candies is the same as that of the visible candies.

Estimation (or estimating) is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. The value is nonetheless usable because it is derived from the best information available.[1] Typically, estimation involves "using the value of a statistic derived from a sample to estimate the value of a corresponding population parameter".[2] The sample provides information that can be projected, through various formal or informal processes, to determine a range most likely to describe the missing information. An estimate that turns out to be incorrect will be an overestimate if the estimate exceeds the actual result[3] and an underestimate if the estimate falls short of the actual result.[4]

The confidence in an estimate is quantified as a confidence interval, the likelihood that the estimate is in a certain range. Human estimators systematically suffer from overconfidence, believing that their estimates are more accurate than they actually are.[5]

  1. ^ C. Lon Enloe, Elizabeth Garnett, Jonathan Miles, Physical Science: What the Technology Professional Needs to Know (2000), p. 47.
  2. ^ Raymond A. Kent, "Estimation", Data Construction and Data Analysis for Survey Research (2001), p. 157.
  3. ^ James Tate, John Schoonbeck, Reviewing Mathematics (2003), page 27: "An overestimate is an estimate you know is greater than the exact answer".
  4. ^ James Tate, John Schoonbeck, Reviewing Mathematics (2003), page 27: "An underestimate is an estimate you know is less than the exact answer".
  5. ^ Alpert, Marc; Raiffa, Howard (1982). "A progress report on the training of probability assessors". In Kahneman, Daniel; Slovic, Paul; Tversky, Amos (eds.). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press. pp. 294–305. ISBN 978-0-521-28414-1.