  We reconsider the inference of spatial power spectra from angular clustering
data and show how to include correlations in both the angular correlation
function and the spatial power spectrum. Inclusion of the full covariance
matrices loosens the constraints on large-scale structure inferred from the APM
survey by over a factor of two. We present a new inversion technique based on
singular value decomposition that allows one to propagate the covariance matrix
on the angular correlation function through to that of the spatial power
spectrum and to reconstruct smooth power spectra without underestimating the
errors. Within a parameter space of the CDM shape Gamma and the amplitude
sigma_8, we find that the angular correlations in the APM survey constrain
Gamma to be 0.19-0.37 at 68% confidence when fit to scales larger than k=0.2h
Mpc^-1. A downturn in power at k<0.04h Mpc^-1 is significant at only 1-sigma.
These results are optimistic as we include only Gaussian statistical errors and
neglect any boundary effects.
