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Is there a formal difference between weighted average and weighted mean?

I get corrected to the latter if I type in the former in wikipedia, and then there is a lot of stuff about the name "average" so I'm not sure about anything anymore.

As a small bonus question: Logically, what is the field which introduces weighted averages? To associate it with measure theory seems a little over the top.

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    Well, instead of setting bonus on your question, you could accept some of the good answers you've got to your previous questions!2012-05-17

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As far as I know average and mean informally are interchangeable terms.

As far as weighing, the classical Greeks already were aware of not only arithmetic, geometric and harmonic means but possibly as many as 10 different types.

As discussed in Graziani and Veronese in "How to compute a mean? The Chisini approach and its applications" Am Stat 2009:

"(Oscar) Chisini in 1929 pointed out that in a practical context a mean should simplify the problem under investigation (by replacing several observations by a single value) so that the overall evaluation of the problem itself remains unchanged. Therefore, the main issue is the specification of the invariance requirement, being a function of the observation, that we want to remain unchanged while replacing the observations by their mean"

The authors also write:

"The approach has a double advantage. First by discouraging any automatic procedure it makes students understand the substance of the problem for which a mean is required. Second, it does not require a preliminary (and necessariy imcomplete) list of different mean formulas."

Examples of invariance requirements and resulting means listed in Table 1 include weighed arithmetic, weighed quadratic, weighed harmonic, weighed geometric, weighed power, weighed exponential.

The paper works through several easily followed practical applications including Mean Traveling Speed, Mean Interest Rate, Mean Exchange Rate and others.

Finally, the authors note that Chisini Mean does not directly address important statistics like mode and median, but generalization by A. Herzel in 1961 (A paper I've been searching for) recasts the invariance constraint as an optimization problem to handle these.

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    Okay Alan that is perfectly reasonable. There is nothing sacred about unbiased estimation and often there is a variance vs bias tradeoff. Estimators with small bias can be more accurate than unbiased estimates The James-Stein estimator of a multivariate normal mean is a classic example. My example was designed to show a justification for a specific weighting scheme.2012-05-19