Youll have to forgive me, folks. Im in the midst of writing a paper (*insert excited Peter Griffin gasp here*– there are lots of open access virology journals, but I cant promise it).
Writing this paper has reminded me of a luxury we have in scientific research: we can be wrong.
Its bad when a physician or nurse is wrong, giving the wrong diagnosis or a wrong drug.
Its bad when a truck driver is wrong, delivering the wrong thing to the wrong place under the wrong shipping conditions.
Its bad when a teacher is wrong, mixing up a battle date or equation or artist (I hated when my teachers/profs messed up, even when they immediately recognized it, cause I would always remember the wrong thing and not the right thing for the rest of the semester!!!).
Its bad when an engineer is wrong, sending an interplanetary probe millions of miles off target.
But as a research scientist, I can be wrong as much as I damn well please. My hypothesis four years ago turned out to be completely and utterly wrong. For a while now, Ive been pretty hangdog, dragging my feet writing this paper, cause I thought I messed up. I thought I was a friggen idiot because my experiments were not matching up to my original hypothesis… they actually pointed towards the opposite of my original hypothesis… ugh…
After talking with Bossman about it, we realized that what I found is exactly what we should have expected. In retrospect, my data makes perfect sense. But if we knew four years ago what we knew now, the experiments would have been pointless!
It doesnt matter in the slightest that I was wrong four years ago– we now know the original hypothesis is wrong, and we have lots and lots of data to explain exactly why that hypothesis is wrong and to point us towards new cool experiments!
The original hypothesis was really just a starting off point. End the end, it doesnt matter whether I was wrong or right, only that our understanding of how HIV-1 works is going to increase after I publish!
We can be wrong! Its okay!
But remember, this luxury is also a responsibility. Never trust a scientist that cannot admit they are wrong.