In catalysis research, data often comes with multiple sources of variation such as differences in experimental conditions, catalyst batches, or measurement errors. Mixed effects models are particularly useful because they can handle complex data structures, allowing for more accurate and reliable results. For example, they can distinguish between the intrinsic activity of a catalyst and the variability due to experimental conditions.
