Home Statistical Dictionary Type I Error

Type I Error

Rejecting a true null hypothesis -- a false positive, with its rate controlled by the significance level alpha.

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In Plain English

A Type I error is a false alarm: your test says there's an effect, but there really isn't one. Setting alpha = 0.05 means accepting a 5% chance of crying wolf, in exchange for being able to detect real effects when they do exist.

Definition

A Type I error occurs when a hypothesis test rejects a true null hypothesis $H_{0}$ . Its probability is denoted $α$ (the significance level), chosen by the researcher before the test is run, which caps the long-run false-positive rate.

Formula

α = P (reject H_{0} ∣ H_{0} true)

Properties

Lowering $α$ reduces Type I errors but increases Type II errors (missed real effects), all else equal.
Running many tests at the same $α$ inflates the overall false-positive rate -- see multiple-comparison corrections.
$α$ is a policy choice made before the test, not something estimated from the data.

At a Glance

Symbol

α

Also calledfalse positive

Trade-off withType II error (

β

)

Typical setting

α = 0.05

Related Terms

P-value

Last updated July 30, 2026← Back to the Dictionary