Triple/N-way collocation is a statistical analysis tool used to estimate the individual error variances of simultaneous observations of a physical quantity by three or more distinct systems. The tool is widely used to validate remote sensing data products such as ocean surface winds and soil moisture retrieved by satellite sensors, where simultaneous observations by different systems are common. However, the method relies on several assumptions about the statistical properties of the observations that are not always valid in a real-world scenario. We test the validity of these assumptions using a numerical simulator and assess their impact on error variance estimates. Some of these assumptions, that the errors are uncorrelated between observing systems or the reference system having a non-unity scaling factor, etc., are found to have a large impact on estimates of error variance when violated. The violation of some other assumptions is found to be less impactful. The simulator also provides corrections to the erroneous estimates of error variances that result when the underlying assumptions are violated. Additionally, we present a new, more general version of the collocation analysis tool that accommodates cases where the error variance in an observing system has a dependence on the true signal.