Supported or not supported – what does that really mean?
In this project, we like to think of the data and analyses (number crunching) as part of a process that leads to conclusions - including, of course, whether a hypothesis (or claim) was supported or not. I, for one, have been harping (annoyingly) on folks not to use terms like “proved”, “right” or “wrong”, and “true” or “false”. Why? What does it matter – it’s just a word?
I think the biggest reason to avoid these black and white, binary terms (yes/no, on/off) is because it’s really not yes or no. It’s really, “My hypothesis was supported by the data” or “Wow, that’s really interesting!” followed by an infinite list of new claims one could test. We don’t like to ascribe a negative connotation to the “not supported” side of things – it’s not, "we were wrong, case closed"; it’s really, "neat, now I need to re-think my model of the world, and I have so many new ideas about what else could be happening".
So, as folks work on finishing up data analysis, posters, and presentations, keep an open mind and don’t get sucked in to the trap of being binary. There’s a whole world of questions out there, waiting to be investigated.