2. Inference using t-distributions
The Statistical Sleuth, chapter 2
T-tests
- useful in regression and analysis of variance structures
- derived under random sampling models when populations are normally distributed
- can function as approximation to randomization tests
- often work quite well even when populations are not quite normal
Ex: Bumpus' Data on Natural Selection (observational)
Evolution - house sparrows survival and death through harsh winter. Is humerus length different for the two populations?
Two-sided p-value: .08 from two-sample t-test. Mean length 0.010 in greater, 95% confidence interval from -0.001 to +0.021.
Observational - no causal inference. Non-random population. (Very weak difference).
Ex: Anatomical abnormalities associated with schizophrenia (observational)
Post-mortem difference in size of brains, after controlling for confounding factors by choosing monozygotic twins.
Two-sided p-value: .006, from paired t-test). Mean volume 0.20 cm3 smaller, (11%), 95% confidence interval from 0.07 to 0.33 cm3.
Not randomly selected, and no causal inference - whether brain size preceded schizophrenia or not.