Statisticscalendar_todayLast updated: Apr 2026
What is Type I & Type II Errors?
/taɪp wʌn ænd taɪp tuː ˈerərz/
In hypothesis testing, a Type I error is a false positive (concluding something is true when it isn't); a Type II error is a false negative (failing to detect something that is actually true).
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Everyday Example
A pregnancy test that says positive when you're not pregnant is a Type I error (false positive). A test that says negative when you are pregnant is a Type II error (false negative). Both have consequences — different ones.
publicReal-World Application
“In medical screening, Type I errors mean healthy people receive unnecessary and stressful treatment. Type II errors mean sick people go undiagnosed. Different diseases justify different trade-offs between these two error types.”
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Did you know?
Statistician Jerzy Neyman and Egon Pearson formalised the framework of Type I and II errors in 1933 to give scientists a rigorous language for evaluating the reliability of experimental conclusions.
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Key Insight
All decisions under uncertainty involve this trade-off. Courts optimise against Type I errors ("innocent until proven guilty"). Fire alarms optimise against Type II errors (better a false alarm than an undetected fire). Neither can be zero.
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