Table 3

Change in prior probabilities of cafestol not affecting serum cholesterol to posterior probabilities using data of the present study and Bayesian analysis

Prior probability

Prior odds (Yes/No)

Posterior odds

Posterior probability


0.90 (very strong)

0.9/(1-0.9) = 9

9x Bayes factor = 3.22

3.22/(1+3.22) = 0.76

0.75 (strong)

0.75/(1-0.75) = 3

3x Bayes factor = 1.07

1.07/(1+1.07) = 0.52

0.50 (equivocal)

0.50/(1-0.50)= 1

1x Bayes factor = 0.36

0.36/(1+1.07) = 0.26

0.25 (weak)

0.25/(1-0.25) = 0.33

0.33x Bayes factor = 0.12

0.12/(1+0.12) = 0.11

0.10 (very weak)

0.10/(1-0.10) = 0.11

0.11x Bayes factor = 0.04

0.04(1+0.04) = 0.04


A priori probabilities were converted to a priori odds and multiplied by the minimum Bayes factor*. The obtained a postiori odds were converted to a postiori probabilities. *Bayes factor = e to the power -Z2/2, where Z is the Z-score corresponding to the P-value for obtaining an effect of 0.27 mmol/l under the null hypothesis. P-value = 0.15, Z-score = 1.43 the minimum Bayes factor = 0.36

Boekschoten et al. Nutrition Journal 2004 3:7   doi:10.1186/1475-2891-3-7

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