As the end of the semester comes near I have found myself reflecting on how far I’ve come since the first class. This was the first graduate class I have taken and after a 2 year hiatus from any school at all I had to make quite the adjustment as I work a full time research job in tandem with class. I had no experience doing any coding at all and I thought I knew a sufficient amount of statistics from my data preparation and embarrassing p-value conclusions in excel. When the R coding conversations kicked off and I still was having trouble knitting a pdf output I became very nervous.
I realized that coding in R was like learning a new language and it reminded me of stories my father told me about moving to Italy in the 1970s without knowing a word of Italian when English was not very common there. He took a language immersion class to learn Italian as quickly as possible in which they spoke only Italian. He became a fluent Italian speaker in only a matter of months because you essentially force yourself to learn it when it’s the only option. Anyways, I tried to treat our class and my time working on homework in this style of thinking in code and ultimately putting the code and new statistics knowledge together piece by piece.
Most of my research experience is in the biotech realm of protein and molecular biology. However, under the same premise as learning code I have actually learned a lot about ecology and environmental biology just from the related readings and surrounding ecologists in class. I have noticed that ecology studies can have a lot more variables at play and this often requires the researcher to take a step back and assess the best way to analyze the data. In my research, I have found myself often pushed in the direction of regimented data analysis to determine a “significant” or “insignificant” difference based on a .05 p-value regardless of the situation. Although data generated in an in vitro laboratory study is usually more controlled and intentionally contains fewer variables than most field studies it is still valuable to think about the relationships and different models.
This default data analysis style I think is dangerous and incorrect because just like science, statistics is an evolving and improving field. This was said perfectly in the introduction of the Ecology Special Section on P Values Forum, “We also need to remember that ‘‘statistics’’ is an active research discipline, not a static tool-box to be opened once and used repeatedly… Continual new developments in statistics allow not only for reexamination of existing data sets and conclusions drawn from their analysis, but also for inclusion of new data in drawing more informative scientific inferences.”
Prior to this class I admittedly thought of statistics as always possessing a right way to do things like many people in immunology and molecular biology, including well-published scientists. Thankfully, being surrounded by ecologists and thinking about complicated data sets has allowed me break free of this thought process and the robot analysis style.