Why? There are lots of researchers who genuinely understand what "statistical significance" means, and how badly it is misused in the literature, and we are trying to get away from reliance on this concept.

There are some problems that need to be studied but are such that no single researcher is going to gather a very big sample. The smaller the sample, the larger the effect size (the difference between the experimental and control groups) it is going to take to achieve statistical significance. It is quite possible to get an effect size time after time that is large enough to be of practical significance, yet never achieve statistical significance. If you see, say 10 studies like this, you are going to be convinced that there is something going on, are you not? You should be, because you can conceive of a set of studies to be an experiment. And if you get 10 out of 10 in a direction and of large enough effect size to be of practical significance, you most assuredly have "significance".

Why am I harping so on this? Because, as you read published studies, you are going to see studies that showed "significance" on one or more variables, but perhaps not on the one the researcher was really interested in. Please do not overlook these "non-significant" results. If you find enough studies with the same thing, you could very well be onto something very useful.

I'll get off my soap box, now.