• Skip to secondary menu
  • Skip to main content
  • Skip to primary sidebar
  • My Store
  • Glossary
  • Home
  • About Me
  • Contact Me

Statistics By Jim

Making statistics intuitive

  • Graphs
  • Basics
  • Hypothesis Testing
  • Regression
  • ANOVA
  • Probability
  • Time Series
  • Fun

Fixed and Random factors

By Jim Frost

In ANOVA, factors are either fixed or random. In general, if the investigator controls the levels of a factor, the factor is fixed. The investigator gathers data for all factor levels she is interested in.

On the other hand, if the investigator randomly sampled the levels of a factor from a population, the factor is random. A random factor has many possible levels and the investigator is interested in all of them. However, she can only collect a random sample of some factor levels.

Suppose you have a factor called “operator,” and it has ten levels. If you intentionally select these ten operators and want your results to apply to just these operators, then the factor is fixed. However, if you randomly sample ten operators from a larger number of operators, and you want your results to apply to all operators, then the factor is random.

These two types of factors require different types of analyses. The conclusions that you draw from an analysis can be incorrect if you specify the type of factor incorrectly.

Related

Synonyms:
Random factors
Related Articles:
  • Repeated Measures Designs: Benefits and an ANOVA Example

Primary Sidebar

Meet Jim

I’ll help you intuitively understand statistics by focusing on concepts and using plain English so you can concentrate on understanding your results.

Read More...

Buy My Introduction to Statistics Book!

Cover of my Introduction to Statistics: An Intuitive Guide ebook.

Buy My Hypothesis Testing Book!

Cover image of my Hypothesis Testing: An Intuitive Guide ebook.

Buy My Regression Book!

Cover for my ebook, Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models.

Subscribe by Email

Enter your email address to receive notifications of new posts by email.

    I won't send you spam. Unsubscribe at any time.

    Follow Me

    • FacebookFacebook
    • RSS FeedRSS Feed
    • TwitterTwitter

    Top Posts

    • How to Interpret P-values and Coefficients in Regression Analysis
    • How To Interpret R-squared in Regression Analysis
    • F-table
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • Weighted Average: Formula & Calculation Examples
    • Z-table
    • Mean, Median, and Mode: Measures of Central Tendency
    • How to do t-Tests in Excel
    • One-Tailed and Two-Tailed Hypothesis Tests Explained
    • Interpreting Correlation Coefficients

    Recent Posts

    • Sum of Squares: Definition, Formula & Types
    • Mann Whitney U Test Explained
    • Covariance: Definition, Formula & Example
    • Box Plot Explained with Examples
    • Framing Effect: Definition & Examples
    • Trimmed Mean: Definition, Calculating & Benefits

    Recent Comments

    • Jerry on Sum of Squares: Definition, Formula & Types
    • Karly on Choosing the Correct Type of Regression Analysis
    • Jim Frost on How to Interpret P-values and Coefficients in Regression Analysis
    • Miriam on How to Interpret P-values and Coefficients in Regression Analysis
    • Klaus on Linear Regression Equation Explained

    Copyright © 2023 · Jim Frost · Privacy Policy