Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_statfun_pooledT”.

```  FT_STATFUN_POOLEDT computes the pooled t-value over a number of replications. The
idea is that you compute a contrast between two conditions per subject The t-values
are pooled over subjects and compared against the pooled pseudo-values. Since
according to H0 the expected t-value for each subject value is zero, the difference
between the pooled t-value and the pseudo-value (which is set to zero) is a
fixed-effects statistic.

The computation of the difference between pooled t-values can be repeated after
randomly permuting the t-values and pseudo-values within the subjects. Each random
permutation gives you an estimate of the difference. The random permutations build
up a randomization distributin, against which you can compare the observed pooled
t-values.

The statistical inference based on the comparison of the observed pooled t-values
with the randomization distribution is not a fixed-effect statistic, one or a few
outlier will cause the randomization distribution to broaden and result in the
conclusion of "not significant".

Use this function by calling one of the high-level statistics functions as
[stat] = ft_timelockstatistics(cfg, timelock1, timelock2, ...)
[stat] = ft_freqstatistics(cfg, freq1, freq2, ...)
[stat] = ft_sourcestatistics(cfg, source1, source2, ...)
with the following configuration option
cfg.statistic = 'ft_statfun_pooledT'

Configuration options that are relevant for this function are
cfg.ivar      = number, index into the design matrix with the independent variable