Not right, but reasonable

The wording used to describe the process of statistical inference is incredibly important. It is not uncommon to hear or see words and phrases like the following in writing and presentation about statistical approaches: the best, the correct, wrong, assumptions are met, the answer, determine, the right approach, etc.

These words are unrealistic, misleading, and usually unjustifiable. Why do we use them?

That’s never an easy question — but I think the most superficial answer is because it’s what we’ve seen modeled by others. And, there is a worry that less strong wording will come across as weak and unconfident — and then the person with the strong words will get the publication, get the promotion, get the grant funding… — you get it. But, this post isn’t about all that could explode from that last sentence. So, I’m letting go of the why for now and just want something much simpler – acknowledgment that we use such words when they are not warranted. Let’s admit it, embrace it, and call each other on it.

The focus has to change from right to reasonable. You are choosing an approach that you think it reasonable for a given problem, and it is your job to justify that opinion to others. Others can then make their own decision about whether your arguments lead them to also conclude it is a reasonable approach.

Reasonable allows space for multiple approaches and perspectives that right does not allow any space for. Think about how this can improve science, as well as humanity.