by Kim Love and Karen Grace-Martin Statistics terminology is confusing. Sometimes different terms are used to mean the same thing, often in different fields of application. Sometimes the same term is used to mean different things. And sometimes very similar terms are used to describe related but distinct statistical concepts.

Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions?

One issue that affects how to interpret regression coefficients is the scale of the variables. In linear regression, the scaling of both the response variable Y, and the relevant predictor X, are both important. In regression models like logistic regression, where the response variable is categorical, and therefore doesn’t have a numerical scale, this only […]

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 […]

Post-hoc tests, pairwise or other linear contrasts, are typical in an analysis of variance (ANOVA) setting to understand which group means differ. They incorporate p-value adjustments to avoid concluding that group means differ when they actually do not. There are several adjustments that can be considered for conducting multiple post-hoc tests, including single-step and stepwise […]

Imputation as an approach to missing data has been around for decades. You probably learned about mean imputation in methods classes, only to be told to never do it for a variety of very good reasons. Mean imputation, in which each missing value is replaced, or imputed, with the mean of observed values of that […]

Effect size statistics are extremely important for interpreting statistical results. The emphasis on reporting them has been a great development over the past decade.

Data analysts can get away without ever understanding matrix algebra, certainly. But there are times when having even a basic understanding of how matrix algebra works and what it has to do with data can really make your analyses make a little more sense.

The Estimated Marginal Means in SPSS GLM are the means of each factor or interaction you specify, adjusted for any other variables in the model.

The practice of choosing predictors for a regression model, called model building, is an area of real craft. There are many possible strategies and approaches and they all work well in some situations. Every one of them requires making a lot of decisions along the way. As you make decisions, one danger to look out […]