At least this is how I used to remember the guy and his distribution. In the period spanning from 2008 to 2011 of my existence, I graduated in biology and never heard so much about a class as the class of biostatistics. All of us aspiring biologists deemed it as one of the toughest. I had prepared for the challenge with the same brisk readiness I did for all other classes. Promptly I realised at the age of 20 that scholars often consider ‘tough’ what in reality is (and should not be) boring. A lot of things went wrong. At a particular practical class on the basic probability theory, the lecturing professor called me out to solve an exercise in the blackboard and laughed at my hardship. His old-fashion teaching method did well in keeping most of my peers away – black and white slides citing dull old manuscripts, speech in monotone mode and little development on the formulas bombarding the white screen in our seminar hall. I was doing t-tests and I did not know its purpose nor how it did work. More surprisingly, considering the state-of-the-art in biostatistics at that time, we had only a five minute-long experience with R during which we learned to extract random numbers and plot shitty histograms. It turned out, at the end of 2009, that I failed biostatistics. The second attempt the year after, going through all over again, felt like having to eat my own vomit. Twice as ‘tough’ as the first attempt though I managed to survive with a very modest score. When it was over I swore I would never go back to it, and happily proceeded to a MSc in molecular biology (2011 to 2013). While I did not go back to it, it did come to me, slowly and gradually, following the boiling frog process. By the end of my master’s I noticed I had learned more theory and practice on my own than I had in those classes. As of today I am a PhD student working on the analysis of biological data.
I look back and wish I had better luck in the beginning – statistics and mathematics are cool and you should know it. With the exponentially growing amount of data worldwide there is a concomitant need for data analysis. With the existing online resources you can learn and master it all for free, using your own machine. This is my contribution for the people that like me felt incapable and frustrated, and I hope these R tutorials will reach the understanding of any interested people from any background. Never give up!