Clinical data still exists that would allow a strategy to stop doing more tests at specific cut off point as the payoff from the hypothesis being right is dependent to the size of the effect and there will be clinical data at some point where the integral of payoff over lost clinical effects is small enough. It just gets fairly annoying to calculate. . Taking the strategy will be similar to gambling decision.
I do agree that there is a place for occam’s razor here but there exist no formalism that actually lets you quantify this weak support. There’s the Solomonoff induction, which is un-computable and awesome for work like putting an upper bound on how good induction can (or rather, can’t) ever be.
Clinical data still exists that would allow a strategy to stop doing more tests at specific cut off point as the payoff from the hypothesis being right is dependent to the size of the effect and there will be clinical data at some point where the integral of payoff over lost clinical effects is small enough. It just gets fairly annoying to calculate. . Taking the strategy will be similar to gambling decision.
I do agree that there is a place for occam’s razor here but there exist no formalism that actually lets you quantify this weak support. There’s the Solomonoff induction, which is un-computable and awesome for work like putting an upper bound on how good induction can (or rather, can’t) ever be.