Many integrated data analysis systems for HPLC instruments, for example those performing peak assignment, operate on the assumption that the recorded signal is valid, which is true in most cases. In a field of data-driven research, it is common to 'trust but verify' historically accumulated data , or re-generate it de novo to minimize discrepancy among data sources or avoid implicit biases. The classic 'garbage in, garbage out' principle can undermine any data-driven system; closedloop experiments like Bayesian optimization are among the most vulnerable. Bayesian optimization algorithms, once misdirected by a false signal, will require several observations (or rounds in batch execution) of data acquisition to self-correct at the cost of time and resources at best and fully degrade at worst. Currently, ensuring correct execution, a common step in computer science, is overlooked among target metrics for the evaluation of self-driving laboratories .