Accumulation bias: how to acknowledge, and how to handle it

Studies accumulate over time and meta-analyses are mainly retrospective. These two characteristics introduce dependencies between the analysis time, at which a series of studies is up for meta-analysis, and results within the series. Dependencies introduce bias —accumulation bias— and invalidate the sampling distribution assumed for p-value tests, thus inflating type-I errors. But dependencies are also inevitable, since for science to accumulate efficiently, new research needs to be informed by past results.In this talk we will study why valid inference is still possible, even if accumulation bias is present, influential and uncorrectable.