There is a lot of skill needed to perform good data analyses. It is not just about statistical knowledge (though more statistical knowledge is always helpful). Organizing your data analysis, and knowing how to do that, is a key skill. 
(more…)
There is a lot of skill needed to perform good data analyses. It is not just about statistical knowledge (though more statistical knowledge is always helpful). Organizing your data analysis, and knowing how to do that, is a key skill. (more…)
If you’ve been doing data analysis for long, you’ve probably had the ‘AHA’ moment where you realized statistical practice is a craft and not just a science. As with any craft, there are best practices that will save you a lot of pain and suffering and elevate the quality of your work. And yet, it’s likely that no one may have taught you these. I know I never had a class on this. (more…)
Ever hear this rule of thumb: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”.
I frequently hear this mis-understood and incorrect “rule.”
We all want rules of thumb even though we know they can be wrong, misleading, or misinterpreted.
Rules of Thumb are like Urban Myths or like a bad game of ‘Telephone’. The actual message gets totally distorted over time.
The Kappa Statistic or Cohen’s* Kappa is a statistical measure of inter-rater reliability for categorical variables. In fact, it’s almost synonymous with inter-rater reliability.
Kappa is used when two raters both apply a criterion based on a tool to assess whether or not some condition occurs. Examples include:
My poor colleague was pulling her hair out in frustration today.
You know when you’re trying to do something quickly, and it’s supposed to be easy, only it’s not? And you try every solution you can think of and it still doesn’t work?
And even in the great age of the Internet, which is supposed to know all the things you don’t, you still can’t find the answer anywhere?
Cue hair-pulling.
Here’s what happened: She was trying to import an Excel spreadsheet into SAS, and it didn’t work.
Instead she got:
When a model has a binary outcome, one common effect size is a risk ratio. As a reminder, a risk ratio is simply a ratio of two probabilities. (The risk ratio is also called relative risk.)
Risk ratios are a bit trickier to interpret when they are less than one.
A predictor variable with a risk ratio of less than one is often labeled a “protective factor” (at least in Epidemiology). This can be confusing because in our typical understanding of those terms, it makes no sense that a risk be protective.