Type I vs Type II error — what's the difference?
easyAnswer
- Type I error (false positive): rejecting H0 when it's actually true.
- Its probability is alpha (significance level, often 0.05).
- Type II error (false negative): failing to reject H0 when H1 is true.
- Its probability is beta; .
- There's a tradeoff — lowering alpha raises beta unless you increase sample size or effect size.
Check yourself — multiple choice
- Type I error = false negative
- Type II error = failing to reject H0 when H1 is true
- Alpha is unrelated to Type I error
Type II = we missed a real effect. .
#hypothesis-testing#power
Practise Statistics Fundamentals
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