Abstract Archaeological sampling is a critical yet inconsistently applied aspect of field methodology. Poorly designed strategies produce biased or irreproducible results, especially when recovery is labor-intensive and research expectations are high. This article addresses that challenge through the lens of spatial microrefuse analysis, using simulation modeling to evaluate current practices and improve sampling design, training, and planning. A review of 27 published microrefuse studies reveals wide variation in sampling strategies, unit sizes, and volumes, with little evidence of statistical justification. To explore the consequences of this variation, I introduce the Archaeological Sampling Experiment Laboratory (tASEL), an open-source simulation tool developed in NetLogo and archived in the CoMSES model library. tASEL allows archaeologists to construct artifact distributions and test random, systematic, or hybrid sampling frames with immediate visual and statistical feedback. I used tASEL to conduct 22,000 virtual sampling experiments across two artifact distributions: a diffuse random scatter and a highly clustered pattern. Results show that sampling performance varies significantly by distribution, sample size, and frame design. Random strategies produced the highest accuracy and lowest bias. I conclude by demonstrating how tASEL can be used in classroom and field contexts to improve sampling literacy and support more robust archaeological practice.
Isaac Ullah (Mon,) studied this question.