Confusing Statistical Concepts

Confusing Statistical Term #13: Missing at Random and Missing Completely at Random

One of the important issues with missing data is the missing data mechanism. You may have heard of these: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR).

The mechanism is important because it affects how much the missing data bias your results. This has a big impact on what is a reasonable approach to dealing with the missing data. So you have to take it into account in choosing an approach.

The concepts of these mechanisms can be a bit abstract.

And to top it off, two of these mechanisms have really confusing names: Missing Completely at Random and Missing at Random.

Missing Completely at Random (MCAR)

Missing Completely at Random is pretty straightforward. What it means is what is [Read more…] about Confusing Statistical Term #13: Missing at Random and Missing Completely at Random

Series on Easy-to-Confuse Statistical Concepts

by Karen Grace-Martin 4 Comments

There are many statistical concepts that are easy to confuse.

Sometimes the problem is the terminology. We have a whole series of articles on Confusing Statistical Terms.

But in these cases, it’s the concepts themselves. Similar, but distinct concepts that are easy to confuse.

Some of these are quite high-level, and others are fundamental. For each article, I’ve noted the Stage of Statistical Skill at which you’d encounter it.

So in this series of articles, I hope to disentangle some of those similar, but distinct concepts in an intuitive way.

Stage 1 Statistical Concepts

The Difference Between:

Stage 2 Statistical Concepts

The Difference Between:

Stage 3 Statistical Concepts

The Difference Between:

Are there concepts you get mixed up? Please leave it in the comments and I’ll add to my list.

The Difference Between Association and Correlation

by Karen Grace-Martin 3 Comments

What does it mean for two variables to be correlated?

Is that the same or different than if they’re associated or related?

This is the kind of question that can feel silly, but shouldn’t. It’s just a reflection of the confusing terminology used in statistics. In this case, the technical statistical term looks like, but is not exactly the same as, the way we mean it in everyday English. [Read more…] about The Difference Between Association and Correlation

The Difference Between Random Factors and Random Effects

by Karen Grace-Martin 6 Comments

Mixed models are hard.

They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.

But they’re also extremely important to understand because many data sets require their use.

Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.

One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect. [Read more…] about The Difference Between Random Factors and Random Effects

The Difference Between Link Functions and Data Transformations

by Kim Love 2 Comments

Generalized linear models—and generalized linear mixed models—are called generalized linear because they connect a model’s outcome to its predictors in a linear way. The function used to make this connection is called a link function. Link functions sounds like an exotic term, but they’re actually much simpler than they sound.

For example, Poisson regression (commonly used for outcomes that are counts) makes use of a natural log link function as follows:

The Difference Between Logistic and Probit Regression

by Karen Grace-Martin 17 Comments

One question that seems to come up pretty often is:

What is the difference between logistic and probit regression?

Well, let’s start with how they’re the same:

Both are types of generalized linear models. This means they have this form: