In the earlier post I discussed merely the Bayesian camp so today we will right that wrong.
Frequentist statistics is helpful in that it enables to arive at general conclusions about a test group in a controlled environment.
Can frequentist statistics be used to make decisions? It can and is being done for example in computer science viz. a-b testing for example in intelligent interaction design which is a discipline aimed at improving user experience vis-a-vis electronic interfaces.
In this particular example Bayes would not have sufficed, at least not in its normal form. A applied methodology can be imagined in which Bayesian strength is determined before and after a use-case for two or multiple interfaces.
The research question would be slightly different from that of the frequentist scientists viz. frequentists have a straightforward method in testing whether there is a significant difference in user experience.
Since Bayesian inference is designed to arrive at degree of belief with regard to prior probability it is more applicable to individual use cases rather than coming to conclusions for a predetermined test group.
Hypothesis: frequentist statistics is used to draw general conclusions from carefully selected test groups, bayesian statistics is used to draw case-specific conclusions from historical data and particular context.