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

Statistics By Jim

Making statistics intuitive

  • Basics
  • Hypothesis Testing
  • Regression
  • ANOVA
  • Time Series
  • Fun
  • Glossary
  • Blog
  • My Store

Significance level

By Jim Frost

The significance level, also denoted as alpha or α, is a measure of the strength of the evidence that must be present in your sample before you will reject the null hypothesis and conclude that the effect is statistically significant. The researcher determines the significance level before conducting the experiment.

The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. Lower significance levels indicate that you require stronger evidence before you will reject the null hypothesis.

Use significance levels during hypothesis testing to help you determine which hypothesis the data support. Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant. In other words, the evidence in your sample is strong enough to be able to reject the null hypothesis at the population level.

Related

Synonyms:
Alpha
Related Articles:
  • How Hypothesis Tests Work: Significance Levels and P-values
« Back to Glossary Index

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
    • Using Applied Statistics to Expand Human Knowledge
    • Variance Inflation Factors (VIFs)
    • Assessing a COVID-19 Vaccination Experiment and Its Results
    • P-Values, Error Rates, and False Positives
    • How to Perform Regression Analysis using Excel
    • Coefficient of Variation in Statistics
    • Independent and Dependent Samples in Statistics

    Recent Comments

    • Samiullah on 7 Classical Assumptions of Ordinary Least Squares (OLS) Linear Regression
    • Javier Gonzalez on The Monty Hall Problem: A Statistical Illusion
    • Micheal on 7 Classical Assumptions of Ordinary Least Squares (OLS) Linear Regression
    • Jim Frost on Using Applied Statistics to Expand Human Knowledge
    • Jim Frost on Using Moving Averages to Smooth Time Series Data

    Copyright © 2021 · Jim Frost · Privacy Policy