Staying in science – getting funding and getting peer reviewed – is tough.
That’s one of my main gripes with creationist simpletons who imply that scientists are uncritical of their peers, and that criticism is directed solely at those who refuse to take their claims at face value. They have no clue whatsoever what they’re talking about.
Every scientific claim, as it’s actually being formulated, must be paved with meticulous attention to detail. The scientist advancing some newly-considered possibility must endure a constant barrage of critiquing, on both the grant application and results publication stages.
It’s for a darn good reason – people, even scientists, are prone to error.
I’m not saying this because I’m complaining, although venting does feel good. Criticism does make us better.
The scientific method is a winnowing process, and the writing process is ripe with revisions and the repeated phrase “it’s not good enough.” Competition of ideas with the tangible results as the decider is how science inevitably moves forward. For the individual researcher however, it is humbling. It IS frustrating. But it’s what we do. Curiosity and enthusiasm began our careers in science, and a driving need to maintain an income reminds us to stick to it.
The human frailty in me craves nothing more than the pat on the back, the kind appraisal, and the reassurance that I know what I’m doing. It doesn’t matter what career you’re in, science or otherwise, but you simply won’t get that form of condescension AND a competitive place in the job market. The only solution is to pay attention to detail, learn how to be thick-skinned, and learn how to write persuasively to grant foundations and scientific journals.
Which brings me to an intriguing book that I came across recently – How to Write a Lot: A Practical Guide to Productive Academic Writing. I’m pretty sure that it will help my grant writing skills, but maybe the grad students out there will benefit from reading it before beginning the grant writing beat. After all, it’s something that they don’t teach you well enough in grad school.
In the last years, the need for using statistical testing in bioscience has grown exponentially and so has the development of statistical software. It is now common that everyone is using some sort of stats in their basic research. Among the skillful biostatisticians, R is the most popular software for data analysis, but not all […]
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