What are Type I and Type II errors?

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Type I Error (False Positive)

  • Occurs when we reject the null hypothesis (H₀) even though it is actually true.

  • In simple words: we think there’s an effect or difference when there really isn’t.

  • Probability of making this error is denoted by α (alpha), also known as the significance level (commonly set at 0.05).

  • Example: A medical test says a healthy person has a disease.

Type II Error (False Negative)

  • Occurs when we fail to reject the null hypothesis (H₀) even though it is actually false.

  • In simple words: we miss detecting a real effect or difference.

  • Probability of making this error is denoted by β (beta). The power of a test is 1 – β, which measures how good the test is at detecting true effects.

  • Example: A medical test fails to detect a disease in a sick person.

Key Differences in a Nutshell

AspectType I ErrorType II Error
DefinitionRejecting a true null hypothesisFailing to reject a false null hypothesis
Also calledFalse positiveFalse negative
Probabilityα (significance level)β
RiskClaiming an effect exists when it doesn’tMissing a real effect
ExampleHealthy person diagnosed sickSick person diagnosed healthy

✅ In summary:

  • Type I = “Crying wolf” (sounding the alarm when there’s no danger).

  • Type II = “Missing the wolf” (failing to see danger when it’s really there).

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