  An important aspect of large-scale structure data analysis is the presence of
non-negligible theoretical uncertainties, which become increasingly important
on small scales. We show how to incorporate these uncertainties in realistic
power spectrum likelihoods by an appropriate change of the fitting model and
the covariance matrix. The inclusion of the theoretical error has several
advantages over the standard practice of using the sharp momentum cut $k_{\rm
max}$. First, the theoretical error covariance gradually suppresses the
information from the short scales as the employed theoretical model becomes
less reliable. This allows one to avoid laborious measurements of $k_{\rm
max}$, which is an essential part of the standard methods. Second, the
theoretical error likelihood gives unbiased constrains with reliable error bars
that are not artificially shrunk due to over-fitting. In realistic settings,
the theoretical error likelihood yields essentially the same parameter
constraints as the standard analysis with an appropriately selected $k_{\rm
max}$, thereby effectively optimizing the choice of $k_{\rm max}$. We
demonstrate these points using the large-volume N-body data for the clustering
of matter and galaxies in real and redshift space. In passing, we validate the
effective field theory description of the redshift space distortions and show
that the use of the one-parameter phenomenological Gaussian damping model for
fingers-of-God causes significant biases in parameter recovery.

Cosmology and Nongalactic Astrophysics;High Energy Physics - Phenomenology