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Contents 1 Type  (?) 2 Level  (?) 3 Definition  (?) 4 Objectives  (?) 5 Questions  (?) 6 Keywords  (?) 7 Monographs  (?) 8 eResources  (?) 9 Criticism  (?) 10 Status  (?) 11 Extend  (?) 12 Related  (?) 13 Notes  (?) 14 Field(s)  (?)

Introduction  (?)
There is a dialogue in the statistics literature as to which is most effective as statistical evidence, a comparative or an eliminative approach.

The Fisherian approach, attributed to Ronald Fisher, infers one hypothesis as valid by statistically eliminating others. This is the statistical approach preferred by modern science, and is more ID-friendly, according to Dembski. A Fisherian approach to ID would be inferring design by elimination of other hypotheses (chance and/or natural forces).

The Bayesian approached, formulated by Thomas Bayes, infers one hypothesis as valid by comparing and deciding which is most probable among possible hypotheses. This approach is analagous to "Occam's razor" and is used by the natural sciences in certain research situations. The Bayesian approach is less ID-friendly, according to Dembski. A design inference employing Bayesian methods would be accomplished by comparing design with chance and/or natural forces and deciding which is more statistically probable.

Type  (?)

Theoria

Level  (?)

ALL (Undergraduate, Graduate, Doctorate, Postdoctorate)

Definition  (?)

Study the strengths and weaknesses of these two statistical approaches.

Objectives  (?)

Learn to employ these approaches effectively in the context of research statistics.

Questions  (?)

• Besides specified complexity, what other methods could employ Fisherian approach to infer design?
• How could the Bayesian approach be used to infer design?

Keywords  (?)

inference, hypothesis validation, statistics, probability, elimination, comparison, Thomas Bayes, Ronald Fisher

Monographs  (?)

The Design Revolution, Chapter 33, William Dembski

eResources  (?)

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Criticism  (?)

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Status  (?)

Current William Dembski, ISCID

Extend  (?)

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Related  (?)

A priori

Notes  (?)

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Field(s)  (?)