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Yesterday afternoon and today are days of high-powered statistics. Stuff I haven't had to deal with too much up until now, now that I've learned a system that lets me efficiently manage my data (the stats package R). I've got five stats books around me, plus an old notebook filled with notes, just to cross-check my ideas and approach. In the end, the stuff I'm developing needs to be as simple as possible to be meaningful, but the process of reaching simplicity is quite complex.
It's good to make my brain work hard. The statistics I'm trying to work through will appear on a poster that I'll present at the upcoming conference in Copenhagen. I'm basically characterizing the growth/development of leafcutter ant colonies, from formation up through the first six months of life. In the grand scheme of things, it won't be the math that draws people in - it will all depend on how well I'm able to convince potential readers that what I'm doing has some broader significance. Why study the ants, why study this subject, they will want to know.
If I can hook them in with a good and reasonable justification, perhaps the more astute and statistically-savvy will scrutinize my methods and help me figure out if my mathematical approach is reasonable and worthwhile.
There are many different ways to analyze any given dataset, and often there is no single correct answer. So the statistician must be drawn back towards the questions of what constitutes the common practice, and what's meaningful to other people. New ideas must be presented against this conservative backdrop, and must be presented well to gain acceptance.
It's good to make my brain work hard. The statistics I'm trying to work through will appear on a poster that I'll present at the upcoming conference in Copenhagen. I'm basically characterizing the growth/development of leafcutter ant colonies, from formation up through the first six months of life. In the grand scheme of things, it won't be the math that draws people in - it will all depend on how well I'm able to convince potential readers that what I'm doing has some broader significance. Why study the ants, why study this subject, they will want to know.
If I can hook them in with a good and reasonable justification, perhaps the more astute and statistically-savvy will scrutinize my methods and help me figure out if my mathematical approach is reasonable and worthwhile.
There are many different ways to analyze any given dataset, and often there is no single correct answer. So the statistician must be drawn back towards the questions of what constitutes the common practice, and what's meaningful to other people. New ideas must be presented against this conservative backdrop, and must be presented well to gain acceptance.
