Blinding is fundamental to medical research. Ideally blinding distributes expectancy effects equally between treatment arms, thus enabling inferences to be made on treatment effects beyond expectancy. However, blinding often fails in practice. We use computational modelling to show how weak blinding, combined with positive treatment expectancy, can lead to an uneven distribution of expectancy effects. We call this ‘activated expectancy bias’ (AEB) and show that AEB can inflate estimates of treatment effects and create false positive findings. To counteract AEB, we introduce the Correct Guess Rate Curve (CGRC), a statistical tool that can estimate the outcome of a perfectly blinded trial based on data from an imperfectly blinded trial. To demonstrate the impact of AEB and the utility of the CGRC on empirical data, we re-analyzed the ‘self-blinding psychedelic microdose trial’ dataset (Szigeti et al., 2021). Results suggest that observed placebo-microdose differences are susceptible to AEB and are therefore at risk of being false positive findings. We present a new blinding integrity assessment tool that is compatible with CGRC and recommend its adoption. We conclude that the existence of a placebo control group is insufficient to control for expectancy effects and placebo-controlled studies are more fallible than conventionally assumed.