Are Human and LLM-Generated Observations Statistically Comparable? Rethinking Sampling Units, Variability, and Statistical Inference

The increasing use of Large Language Models (LLMs) as synthetic respondents, experimental participants, or comparators to human samples and populations raises a critical statistical question: under what conditions can LLM-generated observations support valid inductive reasoning alongside human data? This paper distinguishes between the heterogeneity inherent in human populations and the algorithmic variability of LLMs, emphasising that similarity in observed responses does not necessarily indicate equivalence in the underlying generative processes. Human data are derived from individuals sampled from a target population. In contrast, repeated LLM responses are generated by a probabilistic computational system, with variability influenced by the model, prompt, production parameters, context, repeated runs, and computational environment. Consequently, the nominal number of LLM-generated responses should not be equated with an equivalent number of independent human sampling units. This distinction affects the independence of observations, repeatability, sample size, statistical testing, and the interpretation of human–LLM comparisons. Conventional statistical methods are not inherently unsuitable for LLM-generated data; however, their validity depends on whether the experimental unit, estimand, dependency structure, and methodological assumptions align with the data-generating process. To support practical application, a six-stage methodological framework is proposed: defining the inference goal, identifying units and data-generation processes, assessing replication, validating human–LLM comparability, selecting appropriate statistical analyses, and evaluating the sensitivity, reproducibility, and scope of inference. Human–LLM comparability should be treated as an empirical, estimand-specific property rather than assumed from equal sample sizes, similar means, non-significant differences, or the application of identical statistical tests.