It all depends on what you're experimenting on. I do think there are many teams out there who are (in effect) making decisions with less certainty than this, but they wouldn't want to actually quantify it.

p=0.2 doesn't work too well for ship criteria for medicine.

p=0.2 for "this reordering of the landing text improves the rate conversion events" is fine. People make changes based on less information all the time. Waiting for certainty has its own expenses.

Not that fine. The author claims that neutral changes are not costly for users so the only reason to avoid them is to avoid wasting time/money. But that's not really true. Pointless churn annoys users. If your P threshold isn't low enough then you can get stuck in an endless treadmill of making changes that you thought would have benefit but which don't actually do anything because your threshold for something being considered significant is too loose.

> Pointless churn annoys users.

Sure, excessive rate of change is bad. Even if it's all validated and well-proven.

> If your P threshold isn't low enough then you can get stuck in an endless treadmill of making changes

To measure P, I have to make the change.

There's plenty of situations where accumulating enough data to meet a p<0.05 threshold would take months. If my prior is that it's a reasonably good change and has a decent chance to be helpful, and then I take a measurement that makes that roughly 5x more likely... it's OK to push the button. There are many decisions in business that have to be made with much less information or statistical proof than this.