6.7 Potential Errors When Performing Tests

Part of Inference for Categorical Data: Proportions in AP® Statistics. Read the opening section, then create a free account for the full interactive lesson, practice, and progress tracking.

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Type I and Type II Errors

Type I error

Probability = ${\alpha}$ (the significance level)
Example: Concluding a treatment works when it actually doesn't

Type II error

Probability = ${\beta}$ (depends on the true parameter value)
Example: Concluding no treatment effect when the treatment actually works

Summary table:

	${H_0}$ True	${H_0}$ False
Reject ${H_0}$	Type I Error ( ${\alpha}$ )	Correct (Power = ${1 - \beta}$ )
Fail to reject ${H_0}$	Correct	Type II Error ( ${\beta}$ )

Trade-off: Decreasing ${\alpha}$ (being more strict) reduces Type I error risk but increases Type II error risk.

In this unit

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