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/(10.9) = 9 
9x Bayes factor = 3.22 
3.22/(1+3.22) = 0.76 
0.75 (strong) 
0.75/(10.75) = 3 
3x Bayes factor = 1.07 
1.07/(1+1.07) = 0.52 
0.50 (equivocal) 
0.50/(10.50)= 1 
1x Bayes factor = 0.36 
0.36/(1+1.07) = 0.26 
0.25 (weak) 
0.25/(10.25) = 0.33 
0.33x Bayes factor = 0.12 
0.12/(1+0.12) = 0.11 
0.10 (very weak) 
0.10/(10.10) = 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 Z^{2}/2, where Z is the Zscore corresponding to the Pvalue for obtaining an effect of 0.27 mmol/l under the null hypothesis. Pvalue = 0.15, Zscore = 1.43 the minimum Bayes factor = 0.36 

Boekschoten et al. Nutrition Journal 2004 3:7 doi:10.1186/1475289137 