Confidently Wrong: Why Ignoring Binaries Biases IMF Inference at Large Sample Sizes
Fitting single-star models to photometric samples containing unresolved binaries introduces a systematic negative bias on the high-mass slope ($\alpha$) of the Initial Mass Function (IMF). This bias becomes 'confidently wrong' at large sample sizes ($N$), where the error exceeds statistical uncertainty. The study quantifies this bias using mass-addition and luminosity-addition operators, showing that binary-aware inference is necessary for accurate IMF measurements in large surveys.