As it has been said a picture is worth a thousand words and so it is with graphics too. A well constructed graph can summarize information collected from tens to hundreds or even thousands of data points. But not every graph has the same power to convey complex information clearly.

Just about everyone who does any data analysis has used a chi-square test. Probably because there are quite a few of them, and they’re all useful. But it gets confusing because very often you’ll just hear them called “Chi-Square test” without their full, formal name. And without that context, it’s hard to tell exactly what […]

Missing data are a widespread problem, as most researchers can attest. Whether data are from surveys, experiments, or secondary sources, missing data abounds. But what’s the impact on the results of statistical analysis? That depends on two things: the mechanism that led the data to be missing and the way in which the data analyst […]

One component often overlooked in the ‘Define & Design’ phase of a study, is writing the analysis plan. The statistical analysis plan integrates a lot of information about the study including the research question, study design, variables and data used, and the type of statistical analysis that will be conducted.

What’s the difference between Mixed and Multilevel Models? What about Hierarchical Models or Random Effects models? I get this question a lot. The answer: very little.

Centering a covariate –a continuous predictor variable–can make regression coefficients much more interpretable. That’s a big advantage, particularly when you have many coefficients to interpret. Or when you’ve included terms that are tricky to interpret, like interactions or quadratic terms. For example, say you had one categorical predictor with 4 categories and one continuous covariate, […]

Statistical contrasts are a tool for testing specific hypotheses and model effects, particularly comparing specific group means.

by Danielle Bodicoat Statistics can tell us a lot about our data, but it’s also important to consider where the underlying data came from when interpreting results, whether they’re our own or somebody else’s. Not all evidence is created equally, and we should place more trust in some types of evidence than others.

The field of statistics has a terminology problem. It affects students’ ability to learn statistics. It affects researchers’ ability to communicate with statisticians; with collaborators in different fields; and of course, with the general public. It’s easy to think the real issue is that statistical concepts are difficult. That is true. It’s not the whole truth, […]

What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?