This is an interesting question which has come up at least once on the
This is my argument:
The t statistic is not a consistent estimator of an effect size. That is,
for df -> Inf (for increasing sample size),
var(t) -> 1.
This is because t gradually approximates z, the normal variate, at
infinite degrees of freedom. Instead, let b_hat a parameter estimate
in a linear model (the values in con images), and then we have
for df -> Inf,
var(b_hat) -> 0.
That means b_hat is a consistent estimate of b, as it implies
Prob([b_hat - b] > epsilon) -> 0.
(Actually, it's well known that parameter estimates in a linear model
are consistent, so my argument only concerns t.)
> Why in the second level of analysis in SPM the con images are used
> but not spmT images? I am a biologist and not an expert in
> statistics, thus I need a simplified explanation, if possible.
> Thank you in advance for your responses!
> Sincerely yours,