  In addition to the maximum likelihood approach, there are two other methods
which are commonly used to reconstruct the true redshift distribution from
photometric redshift datasets: one uses a deconvolution method, and the other a
convolution. We show how these two techniques are related, and how this
relationship can be extended to include the study of galaxy scaling relations
in photometric datasets. We then show what additional information photometric
redshift algorithms must output so that they too can be used to study galaxy
scaling relations, rather than just redshift distributions. We also argue that
the convolution based approach may permit a more efficient selection of the
objects for which calibration spectra are required.
