Mean / Variance confusion in save_fits function of optimize_KL

While finishing some plots I was surprised by a very high standard deviation and wanted to check if this could be true. Then I found this:

if mean or std:
            m, s = self.sample_stat(op)
        if mean:
            self._save_fits_2d(m, file_name_base + "_mean.fits", overwrite)
        if std:
            self._save_fits_2d(s, file_name_base + "_std.fits", overwrite)

here: https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_8/src/minimization/sample_list.py#L224

So in the 'if std:' we save something called standard deviation.

But sample_stat returns the mean and the variance:

    def sample_stat(self, op=None):
        """Compute mean and variance of samples after applying `op`.

        Parameters
        ----------
        op : callable or None
            Callable that is applied to each item in the :class:`SampleListBase`
            before it is used to compute mean and variance.

        Returns
        -------
        tuple
            A tuple with two items: the mean and the variance.
        """
        from ..probing import StatCalculator
        if self.n_samples == 1:
            res = self.average(op)
            return res, 0*res
        sc = StatCalculator()
        for ss in self.iterator(op):
            sc.add(ss)
        return sc.mean, sc.var

here: https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_8/src/minimization/sample_list.py#L348

So I think we should take the square root here.... @gedenhof @pfrank I address also @parras, since he worked a lot on the whole optmize-kl and sample-list part of nifty.