# beta

### Member Training: Making Sense of Statistical Distributions

August 1st, 2017 by

Many who work with statistics are already functionally familiar with the normal distribution, and maybe even the binomial distribution.

These common distributions are helpful in many applications, but what happens when they just don’t work?

This webinar will cover a number of statistical distributions, including the:

• Poisson and negative binomial distributions (especially useful for count data)
• Multinomial distribution (for responses with more than two categories)
• Beta distribution (for continuous percentages)
• Gamma distribution (for right-skewed continuous data)
• Bernoulli and binomial distributions (for probabilities and proportions)
• And more!

We’ll also explore the relationships among statistical distributions, including those you may already use, like the normal, t, chi-squared, and F distributions.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

### How to Get Standardized Regression Coefficients When Your Software Doesn’t Want To Give Them To You

October 26th, 2012 by

Standardized regression coefficients remove the unit of measurement of predictor and outcome variables.  They are sometimes called betas, but I don’t like to use that term because there are too many other, and too many related, concepts that are also called beta.

There are many good reasons to report them:

• They serve as standardized effect size statistics.
• They allow you to compare the relative effects of predictors measured on different scales.
• They make journal editors and committee members happy in fields where they are commonly reported. (more…)

### Confusing Statistical Terms #2: Alpha and Beta

December 11th, 2009 by

Oh so many years ago I had my first insight into just how ridiculously confusing all the statistical terminology can be for novices.

I was TAing a two-semester applied statistics class for graduate students in biology.  It started with basic hypothesis testing and went on through to multiple regression.

It was a cross-listed class, meaning there were a handful of courageous (or masochistic) undergrads in the class, and they were having trouble keeping (more…)

### Series on Confusing Statistical Terms

December 3rd, 2009 by

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.

Really.

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.