• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

testing normality

When to Check Model Assumptions

by Karen Grace-Martin 1 Comment

Like the chicken and the egg, there’s a question about which comes first: run a model or test assumptions? Unlike the chickens’, the model’s question has an easy answer.

There are two types of assumptions in a statistical model.  Some are distributional assumptions about the residuals.  Examples include independence, normality, and constant variance in a linear model.

Others are about the form of the model.  They include linearity and [Read more…] about When to Check Model Assumptions

Tagged With: categorical outcome, Censored, Model Assumptions, testing normality, Zero Inflated

Related Posts

  • When Linear Models Don’t Fit Your Data, Now What?
  • Member Training: Types of Regression Models and When to Use Them
  • 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption
  • Confusing Statistical Term #7: GLM

3 Mistakes Data Analysts Make in Testing Assumptions in GLM

by Karen Grace-Martin Leave a Comment

I know you know it–those assumptions in your regression or ANOVA model really are important.  If they’re not met adequately, all your p-values are inaccurate, wrong, useless.

But, and this is a big one, linear models are robust to departures from those assumptions.  Meaning, they don’t have to fit exactly for p-values to be accurate, right, and useful.

You’ve probably heard both of these contradictory statements in stats classes and a million other places, and they are the kinds of statements that drive you crazy.  Right?

I mean, do statisticians make this stuff up just to torture researchers? Or just to keep you feeling stupid?

No, they really don’t.   (I promise!)  And learning how far you can push those robust assumptions isn’t so hard, with some training and a little practice.  Over the years, I’ve found a few mistakes researchers commonly make because of one, or both, of these statements:

1.  They worry too much about the assumptions and over-test them. There are some nice statistical tests to determine if your assumptions are met.  And it’s so nice having a p-value, right?  Then it’s clear what you’re supposed to do, based on that golden rule of p<.05.

The only problem is that many of these tests ignore that robustness.  They find that every distribution is non-normal and heteroskedastic.  They’re good tools, but  these hammers think every data set is a nail.  You want to use the hammer when needed, but don’t hammer everything.

2.They assume everything is robust anyway, so they don’t test anything. It’s easy to do.  And once again, it probably works out much of the time.  Except when it doesn’t.

Yes, the GLM is robust to deviations from some of the assumptions.  But not all the way, and not all the assumptions.  You do have to check them.

3. They test the wrong assumptions. Look at any two regression books and they’ll give you a different set of assumptions.

This is partially because many of these “assumptions”  need to be checked, but they’re not really model assumptions, they’re data issues.  And it’s also partially because sometimes the assumptions have been taken to their logical conclusions.  That textbook author is trying to make it more logical for you.  But sometimes that just leads you to testing the related, but wrong thing.  It works out most of the time, but not always.
Bookmark and Share

tn_assum_lmLearn more about each of the assumptions of linear models–regression and ANOVA–so they make sense–in our new On Demand workshop: Assumptions of Linear Models.

 

Tagged With: Heteroskedacticity, regression assumptions, testing assumptions, testing normality

Related Posts

  • When to Check Model Assumptions
  • The Assumptions of Linear Models: Explicit and Implicit
  • When Linear Models Don’t Fit Your Data, Now What?
  • Centering a Covariate to Improve Interpretability

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Assumptions of Linear Models

Upcoming Free Webinars

The Pathway: Steps for Staying Out of the Weeds in any Data Analysis

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT