A couple of days ago I was trying to nail down p-value, significance levels and null hypotheses. I know that if the p-value is lower than 0.05, the result is significant and the null hypothesis is rejected – but that’s just memorization. I wanted to understand it, know when it should be applied, and be able to explain it to others. I turned to my trusty co-worker, R., to help in this endeavor. (If you prefer you can skip the dorky “Brianne comes to understand p-values” conversation and go right to the “why this shit is important” section below.)
You know how when you’re doing data analysis, you sometimes get sucked right in? You’re working with the raw data, getting it all organized and pretty so you can drop the whole chunk into a statistics program and watch the Passing-Bablocks and Pearsons fly forth onto the computer screen? But it takes a while to do all the organization, data point exclusions (with valid justifications), reprocessing, manually checking the lines and watching the SD and CV calculations to make sure there are no cut/paste errors, double-checking formulae, making sure no single reps in set of mostly double reps are mucking up the whole scheme, but finally! – you’re ready to define the final data set, you click the analyze button and BAM! SLOPE OF 1.01, R=1.00 BEETCHEZ!
*ahem* So, work went well last night. How about you?
Anyway, after getting home from work and the grocery store at 10:30pm I lazed out on the google reader and blog writing. But here are a few things that I’ve been mulling over.