Hi there – my name is Jennifer Catrambone and I am the Director of Evaluation & Quality Improvement at the Ruth M Rothstein CORE Center in Chicago, Illinois. That’s an Infectious Disease Clinic specializing in HIV/AIDS. I’m presenting on my favorite nerdy topic – the what and how of Nonparametric Statistics. I’ve taught both parametric and nonparametric stats at the graduate and undergraduate levels and have done stats consulting. Hang on!! Before you go running away because I used the word Statistics a bunch of times already, let me get a couple more lines out.
It hurts my soul (not like sick puppies or mullets, but still…) when people just reach for the parametric stats, e.g., ANOVAs, T Tests, etc…, without thinking carefully about whether those are the best ones for their data. Why? Because those tests, the parametric ones that we all spent all that time learning in school, are sometimes wildly inappropriate and using them with certain very common kinds of data actually decreases your likelihood of finding that sought-after p<.05. The trick is to match your data set, with its imperfections or unpredictable outliers, to the right kind of stats.
Lesson Learned: So, what situations require nonparametric statistics? They can be broken down into a few major categories:
- The data set is very small. Sometimes that N just does not get to where we want it to be.
- The subgroups are uneven. Perhaps there are many pretests and very few post tests, or maybe you let people self-select which group they were in and no one chose the scary sounding one.
- The data is very skewed. Bell Curve, Schmell Curve.
- Your variables are categorical or ordinal.
There aren’t a lot of resources on Nonparametric Statistics out there. College/grad school statistics textbooks offer minimal information on nonparametric stats, focusing disproportionately on Chi Squares but rarely include info on the post hoc tests that should follow that test. One excellent Nonparametric Stats resource, though published in 1997, is by Marjorie Pett and is entitled, “Nonparametric Statistics for Health Care Research.” The popular stats texts by Gravetter and Wallnau have also introduced decision trees for nonparametric stats that are incredibly useful for determining what test to use.
OK – so all of that being said, the bad news is that many of us just use Parametric Stats because that’s what we know, regardless of the data, and accept that with our messy data, effects will be harder to come by. The great news is that that’s not necessary. Nonparametrics take all that into account and slightly modifies parametric tests (e.g., using medians instead of means), making it so that things like skew and tiny samples are not effect-hiding problems anymore.
Want to learn more? Register for Nonparametric Statistics: What to Do When Your Data Breaks the Rules at Evaluation 2015 in Chicago, IL.
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