I’m not a very good liar, never have been (much to the chagrin of my teenage self). I have a big problem when people outwardly lie or try to deceive people (don’t even get me started on our current political situation). I sometime wonder if this is why I like science to think I make a good scientist, because ultimately we are looking for what’s true about the world. We look at data and see what it tells us about our research questions. A true scientist can have his preconceived views come crashing in around him at any moment if the evidence is there to overturn what he previously thought. There could be books written on “the best science at the time” and the significant repercussions of acting on what we thought was true then, and that we know isn’t true now. This can be really scary to people, knowing that you probably don’t have all the answers or that what you think one day can be completely challenged on the next. While most of the time this makes me feel good about my work, that it is honest and forthright, but there are days when I wish I wasn’t so truthful.
Sometimes it is really annoying to be a person who has to do thing, in this case concerning the data I am working on writing up as a manuscript to be submitted to a journal. Because I am dealing with field data, which is inherently messy, and a diverse community of multiple host and tick species, there are a lot of ways to analyze the data. There are many opinions on what the best way is. Which stats are best suited for the data, how to divide up and compare groups, how to/if transforming some of the data will improve the analysis. My head has been spinning the last couple weeks with all the data that I have available, how to describe it in a way that makes sense, and how to emphasize the patterns we think are the most important.
Unfortunately, the best solution on how to approach some of these issues would diminish the impact of our results a little bit. For instance, the data I have from the immune response assays I did with serum from the rodents needed to be transformed to deal with a handful of individuals who has somewhat strange patterns in the results of their test (I am going to withhold some of the details because this is unpublished work). There were two camps of how to transform the data, and I tried both out. Analyzing one kind of transformed data led to some near significant differences between groups of hosts. The other way showed far less statistically significant differences, and after digesting the pros and cons of each method seemed to be the more correct way to do things. This is pretty annoying because the differences I was seeing in these results we’re super significant and I thought this may diminish the impact of the story I was trying to tell. But in the long run I will feel better about presenting the results I think are the most accurate than cherry-picking the results that I “like” best. Still annoying, though.
This constant tug of war with wanting to find the answers and have mind-blowing results, and scientific integrity is a tough one. You’re not going to always get what you expect and you have to remember that negative or unexpected results are still results and they always teach you something. Whether it’s telling you that the pattern you expected isn’t actually what is happening, shows you that you need to improve your methods, or helps you design your next experiment. Because all scientists have probably experienced this in some way is probably why there is always such an uproar when its found out that someone faked their results. Top researchers have lost their jobs, or at least their credibility for doing dodgy science (for example: http://www.scientificamerican.com/article.cfm?id=fudge-factor). While I am definitely not at that extreme a point with my work, I can understand how tempting it is to present the results you think are true, even if they are not actually true. But that isn’t what being a scientist is about, and I think I’m ok with that.
P.S. The comic found here was shared by some friends on Facebook. It’s funny and true, it can makes the average grad student happy and sad at the same time (hehe)