Lecture 10: Multiple Testing - University of Washington
Lecture 10: Multiple Testing
Goals
? Define the multiple testing problem and related concepts
? Methods for addressing multiple testing (FWER and FDR)
? Correcting for multiple testing in R
Type I and II Errors
Actual Situation "Truth"
Decision
H0 True
Do Not Correct Decision
Reject H0
1 -
Rejct H0
Incorrect Decision Type I Error
H0 False Incorrect Decision
Type II Error
Correct Decision 1 -
" = P(Type I Error) ! = P(Type II Error)
Why Multiple Testing Matters
Genomics = Lots of Data = Lots of Hypothesis Tests
A typical microarray experiment might result in performing 10000 separate hypothesis tests. If we use a standard p-value
cut-off of 0.05, we'd expect 500 genes to be deemed "significant" by chance.
Why Multiple Testing Matters
? In general, if we perform m hypothesis tests, what is the probability of at least 1 false positive?
P(Making an error) = P(Not making an error) = 1 - P(Not making an error in m tests) = (1 - )m
P(Making at least 1 error in m tests) = 1 - (1 - )m
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