What is alpha in hypothesis testing?

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What is alpha in hypothesis testing?

Alpha is the significance level you set before analyzing the data. It acts as the cutoff for deciding whether the observed result is unlikely under the null hypothesis. In practice, you compare the p-value to this threshold and reject the null if the p-value is at or below alpha. A common choice is 0.05, meaning you’re willing to accept a 5% chance of declaring significance when the null is true. This threshold helps control how often you make a Type I error over many repeated tests. It’s not describing the sample size or the observed effect size, which are separate aspects of the study.

Alpha is the significance level you set before analyzing the data. It acts as the cutoff for deciding whether the observed result is unlikely under the null hypothesis. In practice, you compare the p-value to this threshold and reject the null if the p-value is at or below alpha. A common choice is 0.05, meaning you’re willing to accept a 5% chance of declaring significance when the null is true. This threshold helps control how often you make a Type I error over many repeated tests. It’s not describing the sample size or the observed effect size, which are separate aspects of the study.

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