The method I described in BAYES.TXT is intended as a tool for evaluating how consistent various data are with a given set of hypotheses. It is not an evaluation tool for the data itself. Data inputs must be accurate and reliable, otherwise you are likely to get garbage.

For example, take President Reagan's remarks in Dec 1985 about, "Well, I don't suppose we can wait for some alien race to come down and threaten us...." Since this remark was widely reported, we can take it as both accurate (it reflects what Reagan said) and reliable (checking it from several sources gives the same answer). The issue then is consistency with our hypotheses (from BAYES.TXT).

Hypothesis 1: US gov't contact, no disinformation. Reagan's remarks are very inconsistent (20% correlation).

Hypothesis 2: US gov't contact, some disinformation. Reagans remarks are very consistent (80% correlation).

Hypothesis 3: US gov't contact, all disinformation. Reagan's remarks are fairly consistent (60% correlation).

Hypothesis 4: No US gov't contact, no disinformation. Reagan's remarks are fairly consistent (60% correlation).

Hypothesis 5: No US gov't contact, some disinformation. Reagan's remarks are somewhat consistent (40% correlation).

Hypothesis 6: No US gov't contact, all disinformation. Reagan's remarks very inconsistent (20% correlation).

Let's apply these judgements to our model (I picked the initial values for the sake of argument, not because I necessarily endorse them).

Hypotheses Initial Datum Product Revised Value One Value
Hyp 1 10% 20% 2% 3.45%
Hyp 2 30% 80% 24% 41.38%
Hyp 3 25% 60% 15% 25.86%
Hyp 4 20% 60% 2% 20.69%
Hyp 5 10% 40% 4% 6.90%
Hyp 6 5% 20% 1% 1.72%

TOTAL 100% 0.58

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