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Significance level

By Jim Frost

The significance level, also known 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
  • Significance Levels
  • How to Interpret P-values and Coefficients in Regression Analysis
  • How Hypothesis Tests Work: Significance Levels (Alpha) and P values
  • How to Identify the Distribution of Your Data
  • When Can I Use One-Tailed Hypothesis Tests?
  • Examples of Hypothesis Tests: Busting Myths about the Battle of the Sexes

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