Statistical inference using hypothesis testing is ubiquitous in science. Several misconceptions and misinterpretations of p-values have arisen over the years, which can lead to challenges communicating the correct interpretation of results.
statistical significance
Member Training: Writing Up Statistical Results: Basic Concepts and Best Practices
Many of us love performing statistical analyses but hate writing them up in the Results section of the manuscript. We struggle with big-picture issues (What should I include? In what order?) as well as minutia (Do tables have to be double-spaced?). [Read more…] about Member Training: Writing Up Statistical Results: Basic Concepts and Best Practices
Your Questions Answered from the Interpreting Regression Coefficients Webinar
Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.
As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)
We had so many that although I spent about 40 minutes answering [Read more…] about Your Questions Answered from the Interpreting Regression Coefficients Webinar
Member Training: Adjustments for Multiple Testing: When and How to Handle Multiplicity
A research study rarely involves just one single statistical test. And multiple testing can result in more statistically significant findings just by chance.
After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times for each test. When you figure out the probability of Type I error across all the tests, that probability skyrockets.
[Read more…] about Member Training: Adjustments for Multiple Testing: When and How to Handle Multiplicity
Member Training: An Overview of Effect Size Statistics and Why They are So Important
Whenever we run an analysis of variance or run a regression one of the first things we do is look at the p-value of our predictor variables to determine whether
they are statistically significant. When the variable is statistically significant, did you ever stop and ask yourself how significant it is? [Read more…] about Member Training: An Overview of Effect Size Statistics and Why They are So Important