OBJECTIVES: Chronobiology is the science that studies biological rhythms based on direct methods and empirical time series of individual subjects. In laboratory medicine, the factor of time is often underestimated, and no methods currently exist to study biological rhythms in population databases of point-like, real-world data (RWD). METHODS: Retrospective databases (24 months, 2022-2023) were extracted for four measurands (sodium, potassium, chloride and leukocytes) from the emergency laboratory. Two different strategies for data grouping were applied: data clouds (with or without outliers) and population-averaged profiles. Cosinor regression analysis was performed on the grouped data to derive circadian parameters. The parameters obtained here were compared to results from the literature, using direct methods and time series. RESULTS: A total of 409,719 data points were analyzed. All measurands exhibited symmetrical data distributions, except for leukocytes. The data clouds did not visually display rhythmicity, but cosinor analysis revealed a significant circadian rhythm. The removal of outliers had minimal impact on the results. In contrast, population-averaged profiles showed visible rhythmicity, which was confirmed by cosinor analysis with a better goodness-of-fit compared to the data clouds. CONCLUSIONS: Population-averaged profiles have advantages over data clouds in characterizing circadian rhythms and deriving circadian parameters. Population chronobiology, based on RWD, is presented as an alternative to classical individual chronobiology, based on time series and overcomes the limitations of direct methods. Utilizing RWD provides new insights into the relationship between chronobiology and clinical laboratory practice.