Member Training: Confusing Statistical Terms

Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.

Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics. 

Other times, two statistical terms may sound similar to each other, but the nuance makes a difference—for example, is that a general or a generalized linear model you are using there?
And, of course, we have situations where the same term can mean more than one thing, and the reverse, where several different terms can refer to the same thing.

Phew! Join us to review and untangle over thirty of these confusing terms together.

In this webinar you will learn about terms with:

  • Special meanings: random, correlation , significant, odds, confounding, bias, error
  • Nuances: general versus generalized, multiple versus multivariate, pairwise versus listwise deletion
  • Multiple meanings: factor, level, covariate, ANCOVA, control
  • Roughly the same meaning: independent variables, dependent variables, marginal/expected/predicted means

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.

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About the Instructor

Kim is a workshop instructor for The Analysis Factor and owner/lead consultant at K.R. Love Quantitative Consulting and Collaboration.

She has worked as a statistical consultant and collaborator in multiple professional roles, most recently as the associate director of the University of Georgia Statistical Consulting Center.

Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design. She has a B.A. in mathematics from the University of Virginia, and an M.S. and PhD in statistics from Virginia Tech.

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