Nearing Convergence

I’ve been rereading our class blog – so many great pieces and smart colleagues doing interesting work – and I’m dangerously close to getting nostalgic as we come to the end of the term here. Some final thoughts:

As much as I continue to believe R gets its name from its tendency to make one shout “Argh!!” when once again some cryptic error message appears, it is clear from wandering the Internet that R has claimed the statistical throne within the realm of biological/ecological sciences. Having begun the R learning process (which obviously never ends given that the language is a moving target, harrumphs Lucy) will stand us all in good stead as we move forward in our research.

As Sean wrote about last week, I too am struggling with design issues, hoping to be able to do solid research work this summer. When I told my advisor Bob Chen how I was trying to learn enough not discover I had “done it all wrong” after the summer, without missing a beat Bob matter-of-factly said “Oh, you will [do it all wrong]. Count on it.” It was not a criticism or warning, just the blunt observation of a professor who has done and seen a lot of research.

So I’m plugging ahead with my goal of good study design, but doing so knowing that inevitably I’ll discover the weaknesses after the fact, and that the real education begins at that point. I find encouragement in the knowledge that failure is inevitably a part of the journey; that others, far smarter researchers than I, have fallen face-first into the mud, learned from it, picked themselves up, salvaged something from the wreckage, as Jarrett so wryly commented, and carried on to better work. We all will too. But I think we now come strengthened with some powerful knowledge and insights from 607.

Best wishes to all my classmates – may your algorithms converge, may your models fit, and may those wretched p-values allow you to reject the null hypothesis.

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