Counterexamples to a Likelihood Theory of Evidence

understood here, are founded on the Likelihood Principle, which may be viewed as thethesis that LTE applies to the problem of comparing simple hypotheses under theassumption that a background model is true. If what can count as a “background model”is left vague, then the counterexamples to LTE are also counterexamples to theLikelihood Principle.The Likelihood Principle has been vigorously upheld (e.g,. Birnbaum 1962,Royall 1991) in reference to its most important consequence, called Actualism by Sober(1993)—the reasonable doctrine that the evidential support of hypotheses and modelsshould be judged only with respect to data that is actually observed. As Royall (1991)emphasizes in terms of dramatic examples, classical statistical practice has sometimesviolated Actualism, and sometimes in the face of very serious ethical issues. But thelikelihood principle has other consequences besides Actualism, and these might be false.Or, put another way, a theory of evidence may deny the Likelihood Principle, withoutdenying Actualism. Actualism is strictly adhered to in all the examples discussed in thispaper.Section 2 describes what a fit function is, and introduces the idea of a fit-functionprinciple. Likelihood is described as a measure of fit in Section 3, and relationshipbetween the Likelihood Principle and LTE is discussed there. The two sections after thatpresent counterexamples to LTE, first in terms of an example with binary (yes-no)variables, and then in terms of continuous variables (a simple curve fitting problem).Fit FunctionsConsider a simple beam balance device (Fig. 1) on which an object a of unknownmass, θ, is hung at a unit distance from the fulcrum. Then the position of the unit mass onthe right is adjusted until the beam balances. The experiment can be repeated by taking aoff the beam and beginning again. Each repetition is called a trial of the experiment.One can even change the “unit distance” between trials, provided that x is alwaysrecorded as a proportion of that distance. In order to experimentally measure the valuesof postulated quantities, like θ, they must be related to observed quantities, in this case,the distance, x, at which the unit mass is hung to balance the beam.In accordance with standard statistical notation, let X denote the distance variablewhile x refers to its observed value. The outcome of the first trial might be X = 18. Theoutcome of the next trial might be X = 19. It is implausible that the outcomes of acontinuous quantity turn out to have integer values (or it be could that the device has akind of ratchet system that disallows in-between values). X is variable because its valuecan vary from one trial to the next. θ is not variable in this sense because its value doesnot change between trials, eventhough its estimated value maychange as the data accumulate.unit distance xTo mark this distinction, θ isreferred to as an adjustableparameter.aunit massThe standardNewtonian equation relating θand X turns out to be veryFigure 1: A simple beam balance.4