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We previously used the minimum posterior rate, a measure which is not easily generalizable to the case of weighted vectors.
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In addition this framework allows us to formally test hypotheses of interest using the Minimum Posterior Predictive Loss Statistic [10].
These techniques calibrate P values such that an interpretation as minimum Bayes factor or minimum posterior probability is justified.
The minimum posterior probability is then read off the third axis.
For q = 90% the minimum posterior probability is 29.9% (blue line).
Finally, the third axis gives not an exact value for the posterior probability of the null hypothesis but only the minimum posterior probability.
We successfully assigned haplotypes for 95% of all families (minimum posterior probability was 90% and mean posterior probability was greater than 99.9%).
Several attempts have been made to transform P values to minimum Bayes factors and minimum posterior probabilities of the hypothesis under consideration.
It visually transforms P values to minimum posterior probabilities of the null hypothesis and thus avoids complicated calculations.
For example, for q = 50% we obtain a minimum posterior probability of no survival benefit of around 4.5% (red line).
I propose a graphical approach which easily translates any prior probability and P value to minimum posterior probabilities.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com