• 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

checking assumptions

Three ‘Rules’ of Statistical Analysis from Your Statistics Class to Unlearn

by Karen Grace-Martin Leave a Comment

When you are taking statistics classes, there is a lot going on. You’re learning concepts, vocabulary, and some really crazy notation. And probably a software package on top of that.

In other words, you’re learning a lot of hard stuff all at once. 

Good statistics professors and textbook authors know that learning comes in stages. Trying to teach the nuances of good applied statistical analysis to students who are struggling to understand basic concepts results in no learning at all.

And yet students need to practice what they’re learning so it sticks. So they teach you simple rules of application.  Those simple rules work just fine for students in a stats class working on sparkling clean textbook data.

But they are over-simplified for you, the data analyst, working with real, messy data. 

Here are three rules of data analysis practice that you may have learned in classes that you need to unlearn.  They are not always wrong. They simply don’t allow for the nuance involved in real statistical analysis.

The Practices to Unlearn:

1. To check statistical assumptions, run a test. Decide whether the assumption is met by the significance of that test. 

Every statistical test and model has assumptions. They’re very important. And they’re not always easy to verify.

For many assumptions, there are tests whose sole job is to test whether the assumption of another test is being met. Examples include the Levene’s test for constant variance and Kolmogorov-Smirnov test, often used for normality. These tests are tools to help you decide if your model assumptions are being met.

But they’re not definitive.

When you’re checking assumptions, there are a lot of contextual issues you need to consider: the sample size, the robustness of the test you’re running, the consequences of not meeting assumptions, and more.

What to do instead:

Use these test results as one of many pieces of information that you’ll use together to decide whether an assumption is violated.

2. Delete outliers that are 3 or more standard deviations from the mean.

This is an egregious one.

Yes, it makes the data look pretty. Yes, there are some situations in which it’s appropriate to delete outliers (like when you have evidence that it’s an error). Yes, outliers can wreak havoc on your parameter estimates.

But don’t make it a habit. Don’t follow a rule blindly.

Deleting outliers because they’re outliers (or using techniques like Winsorizing) is a great way to introduce bias into your results or to miss the most interesting part of your data set.

What to do instead:

When you find an outlier, investigate it. Try to figure out if it’s an error. See if you can figure out where it came from.

3. Check Normality of Dependent Variables before running a linear model

In a t-test, yes, there is an assumption that Y, the dependent variable, is normally distributed within each group. In other words, given the group as defined by X, Y follows a normal distribution.

ANOVA has a similar assumption: given the group as defined by X, Y follows a normal distribution.

In linear regression (and ANCOVA), where we have continuous variables, this same assumption holds. But it’s a little more nuanced since X is not necessarily categorical. At any specific value of X, Y has a normal distribution. (And yes, this is equivalent to saying the errors have a normal distribution).

But here’s the thing: the distribution of Y as a whole doesn’t have to be normal.

In fact, if X has a big effect, the distribution of Y, across all values of X, will often be skewed or bimodal or just a big old mess. This happens even if the distribution of Y, at each value of X, is perfectly normal.

What to do instead:

Because normality depends on which Xs are in a model, check assumptions after you’ve chosen predictors. 

Conclusion:

The best rule in statistical analysis: always stop and think about your particular data analysis situation.

If you don’t understand or don’t have the experience to evaluate your situation, discuss it with someone who does. Investigate it. This is how you’ll learn.

 

Tagged With: checking assumptions, data analysis practice, dropping outliers, winsorizing

Related Posts

  • A Reason to Not Drop Outliers
  • Best Practices for Organizing your Data Analysis
  • Three Habits in Data Analysis That Feel Efficient, Yet are Not
  • Best Practices for Data Preparation

Eight Data Analysis Skills Every Analyst Needs

by Karen Grace-Martin 2 Comments

It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.

It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.

Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.

[Read more…] about Eight Data Analysis Skills Every Analyst Needs

Tagged With: checking assumptions, Data Analysis, data anlyst, data cleaning, data issues, graphs, interpreting, Research Question, researcher, results, Study design

Related Posts

  • Four Weeds of Data Analysis That are Easy to Get Lost In
  • What to Do When You Can’t Run the Ideal Analysis 
  • Best Practices for Data Preparation
  • Member Training: Data Cleaning

The Problem with Using Tests for Statistical Assumptions

by Karen Grace-Martin 2 Comments

Every statistical model and hypothesis test has assumptions.

And yes, if you’re going to use a statistical test, you need to check whether those assumptions are reasonable to whatever extent you can.

Some assumptions are easier to check than others. Some are so obviously reasonable that you don’t need to do much to check them most of the time. And some have no good way of being checked directly, so you have to use situational clues.

[Read more…] about The Problem with Using Tests for Statistical Assumptions

Tagged With: ANOVA, checking assumptions, levene's test

Related Posts

  • The Steps for Running any Statistical Model
  • What are Sums of Squares?
  • Same Statistical Models, Different (and Confusing) Output Terms
  • Member Training: Elements of Experimental Design

The Distribution of Independent Variables in Regression Models

by Karen Grace-Martin 27 Comments

I often hear concern about the non-normal distributions of independent variables in regression models, and I am here to ease your mind.

There are NO assumptions in any linear model about the distribution of the independent variables.  Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables.  But no, the model makes no assumptions about them.  They do not need to be normally distributed or continuous.

It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values.  A highly skewed independent variable may be made more symmetric with a transformation.

Tagged With: checking assumptions, distribution, independent variable, normality, predictor variable, regression models

Related Posts

  • The Distribution of Independent Variables in Regression Models
  • Eight Ways to Detect Multicollinearity
  • Likert Scale Items as Predictor Variables in Regression
  • Why report estimated marginal means?

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Analyzing Pre-Post Data

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

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