Spurious Precision in Meta-Analysis of Observational Research

Meta-analysis upweights studies reporting lower standard errors and hence more precision. But in observational settings, precision is not given to the researcher. Precision must be estimated, and thus can be p-hacked to achieve statistical significance. Simulations and applications show that spurious precision can invalidate inverse-variance weighting and bias-correction methods based on the funnel
plot. Selection models fail to solve the problem, and common cures to publication bias can become worse than the disease. We introduce a novel approach that addresses spuriousness and limits the resulting bias in meta-analysis.