How can we improve statistical training in archaeological science?

The aim of this paper is to shine light on fundamental statistical concepts that archaeologists do not talk about enough. I argue that more deliberate discussion of these statistical ‘elephants in the room’ can have a positive impact on improving statistical training and on steering us away from perpetuation of poor research practices. 1) Statistical thinking should come first . This will help us break down some of the stigma around numbers and statistics, and set us up for building analytical frameworks that will provide the most informative answers to our research questions. 2) Descriptive and inferential statistics have different interpretative potential. This will clarify how we can move from using tools that only allow us to talk about our studied samples to using tools that enable us to draw inferences about the underlying populations from which the samples derived. 3) p values can be extremely variable . This will help spread awareness about the misuses and misconceptions of Null Hypothesis Significance Testing (NHST) and demonstrate the dangers of using significance thresholds to interpret data. 4) Statistical precision is not the same as measurement precision . This will bring attention to the many different types of uncertainties that are built into archaeological datasets (e.g., statistical precision, instrument measurement error, natural variation),.Recognising this is key for drawing reliable inferences from our data. 5) Meta-analyses and forest plots can be useful for synthesising previous research . This will help spread awareness about the benefit of meta-analyses for creating evidence-driven summaries of previous findings. The discussion draws on examples from isotope archaeology, bioarchaeology, and organic residue analysis to illustrate how switching from a reliance on significance testing to a reliance on effect sizes can improve methodological rigour and the representativeness of our findings. The paper ends with a discussion of the roles and responsibilities of supervisors for creating an effective learning environment for statistical training. This includes, but is not limited to, acknowledging the problems of NHST and advocating for adherence to Open Science principles. Ultimately, the changes suggested in this paper will help us raise discipline-wide standards for quantitative training and improve both the breadth and the depth of archaeological research. • Raising the standard for statistical training in archaeology will improve the breadth and depth of archaeological science. • Improving statistical training can start by discussing five fundamental statistical concepts that archaeologists do not talk about enough. • Supervisors can help make statistical training more effective by advocating for statistical reform and Open Science.