In following critical methods (criteria) of assessing the probability of improvable truths (such as historical propositions), we must assess the potency of its:

- plausibility
^{1} - explanatory power
- explanatory scope
- predictive power
- simplicity
^{2} - nesting
^{3} - track record
^{4} - fruitfulness
^{5} - accord with accepted beliefs
- superiority to rival hypotheses
- dis-confirmed by fewer accepted beliefs
- low chance of an incompatible post-hypotheses

Scholarly lists of disiderata use these methods to determine an inference to the best explanation. These criterion have epistemic value that make the theory “truth-tending” (or *more likely* to be true than not).

^{1} As put by Ratzsch as “internal consistency and compatibility with well-grounded metaphysical beliefs.” Could also be stated as including “mathematical validity.”

^{2} Or, “Occam’s Razor”–finding the explanation with the least hypotheses. Elsewhere stated as “limiting ad hoc-ness.” Something is ad hoc when it is an unnecessary additional hypothesis created to save a given theory.

^{3} Retention of a past working theory whose contents have been emptied of everything but the most necessary datum.

^{4} Ratzsch puts it, “how successful it has been in handling problems in the past.”

^{5} “A theory giving rise to unexpected discoveries … [and] fruitful in suggesting new lines of research or new experiments…” (Ibid.)

**Sources:**

Craig, William Lane. “Visions of Jesus: A Critical Assessment of Gerd Ludemann’s Hallucination Hypothesis” ReasonableFaith.org. N.p., n.d. Web. 16 July 2015. .

Ratzsch, Delvin Lee. “The Competence of Science.” *Science & Its Limits: The Natural Sciences in Christian Perspective*. Downers Grove, IL: InterVarsity, 2000. 90. Print.

**See also:**

McCullagh, C. Behan (1984) *Justifying Historical Descriptions*. Cambridge: Cambridge University Press.

https://en.wikipedia.org/wiki/Historical_method#Statistical_inference and https://en.wikipedia.org/wiki/Historical_method#Argument_from_analogy

*Hume’s Abject Failure: The Argument Against Miracles* by John Earman for the probability calculus (Bayes’ Theorem). Craig presents it here.