  The preferential sampling of locations chosen to observe a spatio-temporal
process has been identified as a major problem across multiple fields.
Predictions of the process can be severely biased when standard statistical
methodologies are applied to preferentially sampled data without adjustment.
Currently, methods that can adjust for preferential sampling are rarely
implemented in the software packages most popular with researchers.
Furthermore, they are technically demanding to design and fit. This paper
presents a fast and intuitive Monte Carlo test for detecting preferential
sampling. The test can be applied across a wide range of data types.
Importantly, the method can also help with the discovery of a set of
informative covariates that can sufficiently control for the preferential
sampling. The discovery of these covariates can justify continued use of
standard methodologies. A thorough simulation study is presented to demonstrate
both the power and validity of the test in various data settings. The test is
shown to attain high power for non-Gaussian data with sample sizes as low as
50. Finally, two previously-published case studies are revisited and new
insights into the nature of the informative sampling are gained. The test can
be implemented with the R package PStestR
