One of the features of the world-wide COVID-19 pandemic over the past eighteen months has been the significance of the role of data and associated predictive data modelling which have governed public policy. At the same time, we have inevitably seen the spread of misinformation (as in false or inaccurate information that is believed to be true) and disinformation (information that is known to be false but is nevertheless spread deliberately), stimulating an infodemic alongside the pandemic. The ability to distinguish between information that can be trusted and information which can’t is key to managing the pandemic, and failure to do so lies behind many of the surges and waves that we have witnessed and experienced. Distinguishing between information and mis/disinformation can be difficult to do. The problem is all too often fuelled by algorithmic amplification across social media and compounded by the frequent shortage of solid, reliable, comprehensive, and unambiguous data, and leads to expert opinions being couched in cautious terms, dependent on probabilities and degrees of freedom, and frustratingly short on firm, absolute outcomes. Archaeological data is clearly not in the same league as pandemic health data, but it still suffers from conclusions drawn on often weak, always incomplete data and is consequently open to challenge, misinformation, and disinformation.