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
  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
  For low-level use, the external interface of this function has to be
    [s,cfg] = ft_statfun_pooledT(cfg, dat, design);
    dat    contains the biological data, Nsamples x Nreplications
           dat must contain fourier representations. 
    design contains the independent variable (ivar), Nreplications x Nvar