T-test
A test of a mean using the t distribution, the right choice when the population variance is unknown and has to be estimated from the sample, as it almost always is.
Where you will meet it
T-test appears in 5 learning outcomes on the CFA syllabus. A few of them:
- Choose the right test for a hypothesis concerning a mean
“With an unknown variance — the usual case — a t-test applies, using the sample standard deviation and n - 1 degrees of freedom.”
- Set out the kinds of heteroskedasticity and their consequences for statistical inference
“Conditional heteroskedasticity, where the variance moves with the independent variables, leaves coefficients unbiased but makes standard errors unreliable, usually too small, so t-tests and the F-test reject too often.”
- Contrast parametric with non-parametric tests and recognise when each is appropriate
“A parametric test concerns a population parameter such as a mean or variance and relies on assumptions about the population's distribution; the z- and t-tests of means and the chi-square and F-tests of variances are examples.”
- Build a multiple regression containing qualitative explanatory variables and explain what its output means
“A t-test on each tells whether the difference is significant.”
See also
Practise it
Practise t-test until it is automatic: questions that explain every wrong option.
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