Multimodel inference

Multimodel inference is a statistical approach that considers multiple plausible models to explain data instead of relying on a single best model. By comparing and averaging these models, it accounts for uncertainty in model selection, leading to more reliable and robust conclusions. This approach recognizes that different models may fit the data well in different ways, and by integrating their insights, it provides a comprehensive understanding of the underlying processes or relationships being studied. Essentially, multimodel inference helps researchers avoid overconfidence in a single model and better reflect the complexity of real-world data.