 Linear mixed-effects models, LMMs, have become increasingly popular in the analysis of biological data due to their flexibility and ability to handle complex data structures. However, there are many challenges associated with their application, including selecting appropriate model structures and interpreting results. In this article, we provide an overview of current methods for the application of LMMs to biological data focusing on the use of information theory and multi-model inference. We also discuss practical solutions and point readers towards relevant literature for more detailed explanations. By following these guidelines, researchers will be able to better understand and interpret the results of their analyses, leading to more reliable and robust inferences. This article was authored by Xavier A. Harrison, Linda Donaldson, Maria Eugenia Corriacano, and others.