Introduction: Missing abstinence outcomes are a universal challenge for tobacco cessation studies. The biases inherent in the complete case analysis, where cases missing outcomes are omitted, are well known. Sophisticated statistical methodologies are available to address these biases but are not widely applied in tobacco cessation trials. Within tobacco cessation research, a widely used strategy is penalized imputation (PI), wherein cases missing cessation outcomes are assigned a "currently using tobacco" status. Statistical basis for the assumed conservativeness of PI: Better statistical methods for addressing missing cessation outcomes may not be widely adopted within the tobacco cessation research community because of the perceived conservativeness of this easily implemented PI approach. When rates of missing outcomes are the same among the different intervention groups, this approach does tend to be more conservative than the complete case analysis. However, this result is highly sensitive to the equivalence of the rates of missing outcomes. Nonconservativeness of PI in application: PI is routinely criticized because of somewhat arbitrary performance and an overall lack of conservativeness in practice. Here, we present elementary statistical arguments demonstrating that, in commonly encountered situations, PI is as likely to lead to liberal estimates as to conservative estimates relative to the complete case analysis. Discussion: PI does not necessarily lead to more conservative or less biased effect estimates. Better statistical methods for addressing missing data need to be adopted within the tobacco cessation research community.