I just finished teaching a Ph.D. level applied statistical methods course here at Hopkins. As part of the course, I gave one “pro-tip” a day; something I wish I had learned in graduate school that has helped me in becoming a practicing applied statistician. Here are the first three, more to come soon.
- A major component of being a researcher is knowing what’s going on in the research community. Set up an RSS feed with journal articles. Google Reader is a good one, but there are others. Here are some good applied stat journals: Biostatistics, Biometrics, Annals of Applied Statistics…
- Reproducible research is a hot topic, in part because a couple of high-profile papers that were disastrously non-reproducible (see “Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology”). When you write code for statistical analysis try to make sure that: (a) It is neat and well-commented - liberal and specific comments are your friend. (b)That it can be run by someone other than you, to produce the same results that you report.
- In data analysis - particularly for complex high-dimensional
data - it is frequently better to choose simple models for clearly defined parameters. With a lot of data, there is a strong temptation to go overboard with statistically complicated models; the danger of overfitting/ over-interpreting is extreme. The most reproducible results are often produced by sensible and statistically “simple” analyses (Note: being sensible and simple does not always lead to higher prole results).