Verifying your Assumptions

Independence

Normality

Homoscedasticity

Linearity

Robustness

A statistical method is said to be "Robust" if it does what it is supposed to do even if the assumptions aren't satisfied. Generally, methods are more robust in large samples than they are with small samples. This is frustrating, because it is only with large samples that you can test the assumptions.

An example is the t-test. The t distribution is only justified if the data are independent and normal, but if there are enough degrees of freedom the t distribution becomes a standard normal so it doesn't make much difference whether you treat the variance as known or estimated, hence the assumption that s2 is distributed as a Chi-squared (which is only true for normal data) is no longer important.


Statistics 2MA3 2002-2003

Statistics 2MA3