• 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

Type I error

By Jim Frost

In a hypothesis test, a type I error occurs when you reject a null hypothesis that is actually true. In other words, a statistically significant test result suggests that a population effect exists but, in reality, it does not exist. The difference you observed in the sample is the product of random sample error.

The probability of committing a type I error equals the significance level you set for your hypothesis test. A significance level of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for alpha. However, if you use a lower value for alpha, you are less likely to detect a true difference if one really exists.

Related

Related Articles:
  • How Hypothesis Tests Work: Significance Levels (Alpha) and P values
  • Interpreting P values
  • When Can I Use One-Tailed Hypothesis Tests?
  • Using Data Mining to Select Regression Models Can Create Serious Problems
  • Why Are P Values Misinterpreted So Frequently?

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 eBook!

New! Buy My Hypothesis Testing eBook!

Buy My Regression eBook!

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
    • Popular
    • Latest
    Popular
    • How To Interpret R-squared in Regression Analysis
    • How to Interpret P-values and Coefficients in Regression Analysis
    • Measures of Central Tendency: Mean, Median, and Mode
    • Normal Distribution in Statistics
    • Multicollinearity in Regression Analysis: Problems, Detection, and Solutions
    • How to Interpret the F-test of Overall Significance in Regression Analysis
    • Understanding Interaction Effects in Statistics
    Latest
    • Sampling Methods: Different Types in Research
    • Beta Distribution: Uses, Parameters & Examples
    • Geometric Distribution: Uses, Calculator & Formula
    • What is Power in Statistics?
    • Conditional Distribution: Definition & Finding
    • Marginal Distribution: Definition & Finding
    • Content Validity: Definition, Examples & Measuring

    Recent Comments

    • Chris Anderson on Guide to Data Types and How to Graph Them in Statistics
    • James on Introduction to Bootstrapping in Statistics with an Example
    • Khursheed Ahmad on Sampling Methods: Different Types in Research
    • Jim Frost on Interpreting Correlation Coefficients
    • Jim Frost on Interpreting Correlation Coefficients

    Copyright © 2022 · Jim Frost · Privacy Policy