mixed models

Mixed models are statistical methods that combine two types of data: fixed effects, which are consistent across all observations (like the overall impact of a treatment), and random effects, which account for variability among different groups or observations (like differences between individual subjects). This approach is useful when data is collected in groups or clusters, allowing researchers to analyze complex situations where both common trends and individual variations exist simultaneously. Mixed models help provide a more accurate understanding of data by recognizing both the overarching patterns and the unique characteristics of specific groups.