Depends on why you need to trust something. If the failure state is multiple nukes going off, sure throw money and time at it until your systems are bullet proof. If you've purchased new shoes and need to design a study to figure out which foot you should start with when lacing up, maybe you're going too hard. What about something more chemical in nature; say you have a family recipe you and your family love. You might tinker around with it, but do you stop making it because you are upset by the intellectual sloth of not running double blind studies on Grandma's Famous Cumin Chili? No. Unless something slaps your Bayesian priors in the face, like a news report that says Chili recipes using Cumin might cause cancer.

Again, the idea is that we all come into this with Bayesian priors. You don't need (and will never get) 100% certainty to operate in the real world. I don't need to spend a few hours and $60 to go from 99.5% to 99.7% on this issue and I'm betting you don't either.

We will be, and largely are wrong about a ton of things. And in the majority of cases it doesn't matter one tiny bit. Like here. This is a study about a political flogging point that is 15 years past the point it conceivably could have been made into legislation. The study will be dated by the time there's a political push for the issue to be re-examined.

This is shoelace study v.2.0. We have more pertinent, more complicated and more impactful problems to study both on an institution and personal level, so why devote our limited mental processing time to this? Because you want to be "right"?

Have you ever read a recipe you've done before and thought to yourself "I really shouldn't make this, because I haven't presented this foo