You are correct - this is terrible advice. The main issue is that the word "significance" is overloaded with two meanings in research & stats: "Large and impo

You are correct - this is terrible advice. The main issue is that the word "significance" is overloaded with two meanings in research & stats: "Large and important" (e.g. "clinical significance", often given as an effect size, or some benefit/cost tradeoff) "P value below alpha" (e.g "statistical significance", Probabilty of rejecting H0, which is roughly equivalent to "if the true effect size were zero, what's the chance I could see this effect size in my data given random fluctuactions [and a bunch of other assumptions]"