Peirce's Criterion for the Elimination of Suspect Experimental Data.

Peirce's criterion is a rigorous method based on probability theory that can be used to rationally eliminate outlying or spurious data from a set of experimental measurements. Currently, a method known as Chauvenet's criterion is used in many educational institutions and laboratories to perform this function. Although Chauvenet's criterion is well established, it makes ah arbitrary assumption concerning the rejection of the data, but Peirce's criterion does not. In addition, Chauvenet's criterion makes no distinction between one or several suspicious data values, but Peirce's criterion is a rigorous theory that can be easily applied to several suspicious data values. This paper describes the application of both Peirce's and Chauvenet's methods to a set of data measurements and shows the different results returned by each method. [ABSTRACT FROM AUTHOR]

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