Objectives: If participants can anticipate the intervention, they may alter their responses prior to exposure to treatment. One often-ignored consequence of these “anticipatory effects” (AE) is an impact on the pre-treatment measurement. We explore this potential contamination and present practical options for mitigating AE. Methods: A multidisciplinary review of AE. Results: Pre-treatment measures, especially pre-treatment dependent variables, can be contaminated by AE. Experimenters need to understand the following: (1) When did the treatment ‘commence’? (2) How is the pretest measured? (3) Are AE specific or global? (4) What conclusions can we draw where pretest measures are contaminated by AE? Conclusions: AE are often ignored for both research and policy, which may lead to erroneous conclusions regarding effectiveness, benefits being underestimated, or both. AE can be resolved by collecting ‘clean’ baseline measures prior to the commencement of the AE, but the first step is to be aware of the potential bias due to this treatment × pre-measurement interaction.
- Anticipatory effects
- Pretest measures
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