A fundamental goal of archaeologists is to infer the behaviour of past humans from the attributes of the artefacts they left behind. The archaeological record is, however, fragmented and often provides a partial record of the total artefacts produced by a given population. In turn, there is potential for population-level morphometric data, and therefore behavioural inferences, to be biased relative to the trends realised in the past. This includes morphological range data which are important for identifying similarities and differences between artefact groups, and for contextualising artefacts relative to external variables such as human anatomy, ecology, climate and chronology. Here, we investigate whether optimal linear estimation (OLE) modelling can be used to accurately identify the upper and lower limits of artefact morphological ranges (including those represented by sparse datasets). First, we test whether OLE reliably identifies morphological ranges using randomly sampled subsets of ‘known and complete’ replica artefact assemblages. Using morphometric data from lithic, ceramic, and metal archaeological case studies, we then identify how much further the upper and lower form limits of these artefact types would have been in the past, relative to the ranges evidenced by excavated (i.e., known partial) records. Validation tests demonstrate OLE to be capable of providing broadly accurate estimates for the true morphological range of artefact assemblages. Estimate accuracy increases relative to the percentage of the total assemblage used and the method is shown to function well using as few as five records (k) from an assemblage. The case studies reveal how OLE can overhaul or reinforce our understanding of artefact morphological ranges. In some instances, it is clear that the archaeological record provides a highly accurate representation of artefact morphological ranges and the overlap between artefact groups. For others, it is demonstrated that our understanding of the extreme artefact forms produced by past people is likely inaccurate

考古学家的一个基本目标是根据过去人类留下的文物的属性来推断他们的行为。然而，考古记录是支离破碎的，并且通常只提供特定人群生产的全部文物的部分记录。反过来，群体水平的形态测量数据以及行为推断可能相对于过去实现的趋势存在偏差。这包括形态范围数据，这些数据对于识别文物组之间的相似性和差异以及将文物与外部变量（例如人体解剖学、生态学、气候和年代学）相关联非常重要。在这里，我们研究是否可以使用最优线性估计（OLE）建模来准确识别人工制品形态范围（包括稀疏数据集表示的范围）的上限和下限。首先，我们测试 OLE 是否使用“已知且完整”的复制品组合的随机采样子集可靠地识别形态范围。然后，利用来自石器、陶瓷和金属考古案例研究的形态测量数据，我们可以确定这些文物类型的上限和下限在过去相对于挖掘出的范围（即已知的部分）有多远记录。验证测试表明 OLE 能够对人工制品组合的真实形态范围提供广泛准确的估计。相对于所使用的总组合的百分比，估计准确度会增加，并且该方法在使用组合中的少至五个记录 ( k ) 时效果良好。案例研究揭示了 OLE 如何彻底改变或加强我们对人工制品形态范围的理解。在某些情况下，考古记录显然提供了人工制品形态范围和人工制品组之间重叠的高度准确的表示。对于其他人来说，这表明我们对过去人们制造的极端人工制品形式的理解可能是不准确的