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This is an interesting question which has come up at least once on the  
list previously.

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,
> Vladimir
>