Can you look at experimental results along the way or not?

I'm actually a big fan of certain alpha spending approaches. In one of them (I forget the name), you basically peek along the way, only looking for massive signs, then at the end you get to do a nearly-ordinary analysis. Like, you have to turn p=0.05 into p=0.045 at the end but it's pretty negligible so you don't have to treat the data too carefully in general. You just get to promise nagging partners that yes if it's wildly good (or bad), we've accounted for that and will stop early.

&gt; I’d recommend taking either a Frequentist approach or a Bayesian approach, but not complicating things by hybrid approaches such as alpha spending or designing Bayesian experiments to have desired Frequentist operating characteristics. The middle ground is more complicated and prone to subtle mistakes, though we can help you navigate this middle ground if you need to.<p>Probably not a compelling conclusion to what’s probably a reference article to promote a consultancy

&lt;strike&gt;This article confuses more than it informs.&lt;&#x2F;strike&gt;<p>The answer to the question whether one can look at experimental data in A&#x2F;B tests (or by analogy clinical trials) can be answered with yes even though the article doesn&#x27;t make this very clear. Instead, it gets lost in superficial Frequentist&#x2F;Bayesian (keyword) name dropping.<p>The concept of adjusting for early checking is interesting but this article is less useful than just looking at the the original alpha spending paper: <a href="https:&#x2F;&#x2F;pubmed.ncbi.nlm.nih.gov&#x2F;7973215&#x2F;" rel="nofollow">https:&#x2F;&#x2F;pubmed.ncbi.nlm.nih.gov&#x2F;7973215&#x2F;</a><p>For a Bayesian approach: <a href="https:&#x2F;&#x2F;onlinelibrary.wiley.com&#x2F;doi&#x2F;abs&#x2F;10.1002&#x2F;sim.4780040412" rel="nofollow">https:&#x2F;&#x2F;onlinelibrary.wiley.com&#x2F;doi&#x2F;abs&#x2F;10.1002&#x2F;sim.47800404...</a>