It's not a matter of life and death, I agree - to some extent. Startups have very limited resources, and ignoring inconclusive results in the long term means you're spending these resources without achieving any bottom line results. If you do that too much/too long, you'll run out of funding and the startup will die.

The author didn't go into why companies do this (ignoring or misreading test results). Putting lack of understanding aside, my anecdotal experience from the time I worked as a data scientist boils down to a few major reasons:

- Wanting to be right. Being a founder requires high self-confidence, that feeling of "I know I'm right". But feeling right doesn't make one right, and there's plenty of evidence around that people will ignore evidence against their beliefs, even rationalize the denial (and yes, the irony of that statement is not lost on me); - Pressure to show work: doing the umpteenth UI redesign is better than just saying "it's irrelevant" in your performance evaluation. If the result is inconclusive, the harm is smaller than not having anything to show - you are stalling the conclusion that your work is irrelevant by doing whatever. So you keep on pushing them and reframing the results into some BS interpretation just to get some more time.

Another thing that is not discussed enough is what all these inconclusive results would mean if properly interpreted. A long sequence of inconclusive UI redesign experiments should trigger a hypothesis like "does the UI matter"? But again, those are existentially threatening questions for the people in the best position to come up with them. If any company out there were serious about being data-driven and scientific, they'd require tests everywhere, have external controls on quality and rigour of those and use them to make strategic decisions on where they invest and divest. At the very least, take them as a serious part of their strategy input.

I'm not saying you can do everything based on tests, nor that you should - there are bets on the future, hypothesis making on new scenarios and things that are just too costly, ethically or physically impossible to test. But consistently testing and analysing test results could save a lot of work and money.