  Interferometric observables are strongly correlated, yet it is common
practice to ignore these correlations in the data analysis process. We develop
an empirical model for the correlations present in Very Large Telescope
Interferometer GRAVITY data and show that properly accounting for them yields
fainter detection limits and increases the reliability of potential detections.
We extracted the correlations of the (squared) visibility amplitudes and the
closure phases directly from intermediate products of the GRAVITY data
reduction pipeline and fitted our empirical models to them. Then, we performed
model fitting and companion injection and recovery tests with both simulated
and real GRAVITY data, which are affected by correlated noise, and compared the
results when ignoring the correlations and when properly accounting for them
with our empirical models. When accounting for the correlations, the faint
source detection limits improve by a factor of up to $\sim 2$ at angular
separations $> 20~\rm{mas}$. For commonly used detection criteria based on
$\chi^2$ statistics, this mostly results in claimed detections being more
reliable. Ignoring the correlations present in interferometric data is a
dangerous assumption which might lead to a large number of false detections.
The commonly used detection criteria (e.g. in the model fitting pipeline
CANDID) are only reliable when properly accounting for the correlations;
furthermore, instrument teams should work on providing full covariance matrices
instead of statistically independent error bars as part of the official data
reduction pipelines.
