Image Credit: Grand Velas Riviera Maya, CC BY-SA 2.0, Image Cropped
In ecological studies, the quality of the data we use is often a concern. For example, individual animals may be cryptic and hard to detect. Certain sites that we should really be sampling might be hard to reach, so we end up sampling more accessible, less relevant ones. Or it could even be something as simple as recording a raven when we’re really seeing a crow (check our #CrowOrNo if you have problems with that last one). Modeling approaches aim to mitigate the effect on our results of these shortcomings in the data collection.
However, even if we had perfect data, when we decide how to model that data, we have to make choices that may not match the reality of the scenario we are trying to understand. Model mis-specification is a generic term for when our model doesn’t match the processes which have generated the data we are trying to understand. It can lead to biased estimates of covariates and incorrect uncertainty quantification.