Background Different analytical approaches can influence the associations estimated in observational studies. We assessed the variability of effect estimates reported within and across observational studies evaluating the impact of alcohol on breast cancer. Methods We abstracted largest harmful, largest protective and smallest (closest to the null value of 1.0) relative risk estimates in studies included in a recent alcohol–breast cancer meta-analysis, and recorded how they differed based on five model specification characteristics, including exposure definition, exposure contrast levels, study populations, adjustment covariates and/or model approaches. For each study, we approximated vibration of effects by dividing the largest by the smallest effect estimate [i.e. ratio of odds ratio (ROR)]. Results Among 97 eligible studies, 85 (87.6%) reported both harmful and protective relative effect estimates for an alcohol–breast cancer relationship, which ranged from 1.1 to 17.9 and 0.0 to 1.0, respectively. The RORs comparing the largest and smallest estimates in value ranged from 1.0 to 106.2, with a median of 3.0 [interquartile range (IQR) 2.0–5.2]. One-third (35, 36.1%) of the RORs were based on extreme effect estimates with at least three different model specification characteristics; the vast majority (87, 89.7%) had different exposure definitions or contrast levels. Similar vibrations of effect were observed when only extreme estimates with differences based on study populations and/or adjustment covariates were compared. Conclusions Most observational studies evaluating the impact of alcohol on breast cancer report relative effect estimates for the same associations that diverge by >2-fold. Therefore, observational studies should estimate the vibration of effects to provide insight regarding the stability of findings.