[nickhuh]

Actually, in the frequentist paradigm you could choose to run a sequential hypothesis test which will end when you've acquired sufficient data[1]. Or, if you want to get fancy you could use a multi-armed bandit approach which is probably optimal in many situations in perhaps a more robust way than many Bayesian methods[2]. Really both can work well. My advice is, use whichever you know well enough to utilize effectively!

[1]: https://en.m.wikipedia.org/wiki/Sequential_analysis

[2]: https://en.m.wikipedia.org/wiki/Multi-armed_bandit

[paulddraper]

Right, as I said, it can be done with frequentist statistics. This is what Optimizely does (http://pages.optimizely.com/rs/optimizely/images/stats_engin...). But (1) it is not simple and (2) it is not optimal.

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Agreed 100% about multi-armed bandit which is what I was referring to. And the canonical solutions are in fact Bayesian :) See the Google Analytics link or lookup "Thompson sampling"

From your Wikipedia link:

"Probability matching strategies are also known as Thompson sampling or Bayesian Bandits, and surprisingly easy to implement if you can sample from the posterior for the mean value of each alternative."

[nickhuh]

Oh yeah there are a few Bayesian methods which work great, Bayes-UCB is another. Personally though I think KL-UCB or just plain old UCB would be the ones I'd choose. Like I said earlier, I think these techniques are like programming languages: choose the one you know well enough to get the job done with it.

[kgwgk]

> Actually, in the frequentist paradigm you could choose to run a sequential hypothesis test which will end when you've acquired sufficient data

And, as he said, you have to make adjustments to account for these interim analysis.