• 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

Effect

By Jim Frost

The effect is the difference between the true population parameter and the null hypothesis value. Effect is also known as population effect or the difference. For example, the mean difference between the health outcome for a treatment group and a control group is the effect.

The true population parameter is not known. Consequently, samples are taken and a statistical test, such as a t-test or a one-way ANOVA, determines whether an effect exists and estimates its size.

Related

Related Articles:
  • Nonparametric Tests vs. Parametric Tests
  • How to Interpret P-values and Coefficients in Regression Analysis
  • How Hypothesis Tests Work: Significance Levels (Alpha) and P values
  • Interpreting P values
  • Curve Fitting using Linear and Nonlinear Regression

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
    • Mean, Median, and Mode: Measures of Central Tendency
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • How to Interpret the F-test of Overall Significance in Regression Analysis
    • Choosing the Correct Type of Regression Analysis
    • How to Find the P value: Process and Calculations
    • Interpreting Correlation Coefficients
    • How to do t-Tests in Excel
    • Z-table

    Recent Posts

    • Fishers Exact Test: Using & Interpreting
    • Percent Change: Formula and Calculation Steps
    • X and Y Axis in Graphs
    • Simpsons Paradox Explained
    • Covariates: Definition & Uses
    • Weighted Average: Formula & Calculation Examples

    Recent Comments

    • Dave on Control Variables: Definition, Uses & Examples
    • Jim Frost on How High Does R-squared Need to Be?
    • Mark Solomons on How High Does R-squared Need to Be?
    • John Grenci on Normal Distribution in Statistics
    • Jim Frost on Normal Distribution in Statistics

    Copyright © 2023 · Jim Frost · Privacy Policy