Series on Confusing Statistical Terms

One of the biggest challenges in learning statistics and data analysis is learning the lingo.  It doesn’t help that half of the notation is in Greek (literally).

The terminology in statistics is particularly confusing because often the same word or symbol is used to mean completely different concepts.

I know it feels that way, but it really isn’t a master plot by statisticians to keep researchers feeling ignorant.


It’s just that a lot of the methods in statistics were created by statisticians working in different fields–economics, psychology, medicine, and yes, straight statistics.  Certain fields often have specific types of data that come up a lot and that require specific statistical methodologies to analyze.

Economics needs time series, psychology needs factor analysis.  Et cetera, et cetera.

But separate fields developing statistics in isolation has some ugly effects.

Sometimes different fields develop the same technique, but use different names or notation.

Other times different fields use the same name or notation on different techniques they developed.

And of course, there are those terms with slightly different names, often used in similar contexts, but with different meanings. These are never used interchangeably, but they’re easy to confuse if you don’t use this stuff every day.

And sometimes, there are different terms for subtly different concepts, but people use them interchangeably.  (I am guilty of this myself).  It’s not a big deal if you understand those subtle differences.  But if you don’t, it’s a mess.

And it’s not just fields–it’s software, too.

SPSS uses different names for the exact same thing in different procedures.  In GLM, a continuous independent variable is called a Covariate.  In Regression, it’s called an Independent Variable.

Likewise, SAS has a Repeated statement in its GLM, Genmod, and Mixed procedures.  They all get at the same concept there (repeated measures), but they deal with it in drastically different ways.

So once the fields come together and realize they’re all doing the same thing, people in different fields or using different software procedures, are already used to using their terminology.  So we’re stuck with different versions of the same word or method.

So anyway, I am beginning a series of blog posts to help clear this up.  Hopefully it will be a good reference you can come back to when you get stuck.

We’ve expanded on this list with a member training, if you’re interested.

If you have good examples, please post them in the comments.  I’ll do my best to clear things up.


Why Statistics Terminology is Especially Confusing

Confusing Statistical Term #1: Independent Variable

Confusing Statistical Terms #2: Alpha and Beta

Confusing Statistical Term #3: Levels

Confusing Statistical Terms #4: Hierarchical Regression vs. Hierarchical Model

Confusing Statistical Term #5: Covariate

Confusing Statistical Term #6: Factor

Same Statistical Models, Different (and Confusing) Output Terms

Confusing Statistical Term #7: GLM

Confusing Statistical Term #8: Odds

Confusing Statistical Term #9: Multiple Regression Model and Multivariate Regression Model

Confusing Statistical Term #10: Mixed and Multilevel Models

Confusing Statistical Terms #11: Confounder

Six terms that mean something different statistically and colloquially

Confusing Statistical Term #13: MAR and MCAR Missing Data


Reader Interactions


  1. yutong Liu says

    Hello, I’m seeking clarification about the terminology of “fixed effects” and “random effects”. In methods such as multi-level regression analysis and dynamic structural equation modeling (DSEM), it’s common to report the results in terms of these “fixed effects” and “random effects”. However, as they often appear together, this can become quite confusing. I’m hoping you can help clarify this for me. Thank you very much in advance!

  2. JML says

    Great series. How about clarifying part, partial, and semi-partial? This is a classic example of where different fields/programs use different terminology for the same thing, or the same terms for different things! A handy reference would be most helpful. Thanks!

  3. Karen says

    Denis–Thanks. I’ll get on those.

    Jim–Thanks. As someone trained in psychology and statistics (in a statistics department), I’ve always found econometricians to be using entirely different terminology. But with a bit of explanation, we can often find the common concepts to the different words, and work well together.

    But engineers seem to be speaking a whole different language. LOL. I had a conversation once with an engineer where I had no idea what he was talking about until I finally realized he meant “variable” when he said “parameter.” That was bizarre.

  4. Jim Hurdle says

    Wonderful idea to publish this topic. It actually could almost be extended to a book. I am an economist so I have the “discipline” terms used in my graduate education. Sometimes, in reviews of articles by mathematical statisticians, I find some confusing terminology that turns out to be just a renaming of a concept I already know.

    So keep on this route, your contribution will be appreciated!

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