## Appendix A
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cognitive Factors** & **General Requirements for Operator** & **Examples** \\ \multicolumn{2}{c}{**Support Strategies**} & **The frequency of this fault have only been determined for milk chocolate but not for caramel chocolate” \\ \hline \multicolumn{2}{c}{Provide hints when operators have ignored potentially relevant information} & “You have not checked motor current trend charts, yet” \\ \cline{2-3} \multicolumn{2}{c}{Support the generation of arguments against a given anchor or standard} & “Please check whether the temperature value from the selected previous case applies to the current situation” \\ \hline \multirow{4}{*}{Availability heuristic} & Make information available in an unbiased manner & Provide different data sources (e.g., temperature in end cooler, buffer, and packaging machine) and data types (e.g., temperature, soiling, and motor currents) \\ \cline{2-3}  & Provide information about the base rates of process states and events & “Hollow bottoms are present in 20\% of the bars overall” \\ \cline{2-3}  & Provide hints that other information sources are available & “The current hypothesis can also be checked by inspecting the motor current trend chart” \\ \hline \multirow{4}{*}{Conditional sampling} & Make it explicit when events may be overrepresented in samples & “Chocolate bars have mostly been checked for hollow bottoms when faults have occurred, which may overestimate the impact of this deviation” \\ \cline{2-3}  & Use presentation formats that aid Bayes reasoning & Frequency grids or frequency trees \\ \hline \multirow{4}{*}{Repeating choices that initially led to good outcomes} & Provide hints about own previous choices and choices of others in similar situations & “In previous instances of this fault, you have only checked for hollow bottoms. Other operators have also checked motor current trend charts and vacuum suction” \\ \cline{2-3}  & Make the relevance of different data sources and observations transparent & “Bar skewness after the stopper is predictive of this fault, while bar skewness before the stopper is not” \\ \cline{2-3}  & Make it explicit when the contribution of data sources is context-dependent & “Room temperature is predictive of this fault for milk chocolate but not dark chocolate” \\ \hline \multirow{4}{*}{Salient cues} & Selecting and ignoring particular types of information \\ \cline{2-3}  & Make cue validities explicit & “Hollow bottoms have low predictive value for this fault” \\ \cline{2-3}  & Support evaluations of whether extreme values are relevant & “High room temperature is irrelevant for bad packaging quality” \\ \cline{2-3}  & Make non-salient but relevant cues accessible & Provide height measurement of chocolate bars, which can cause problems but is not visually perceivable for operators \\ \hline \multirow{4}{*}{Confirmation bias} & Provide evidence/data in favor of opposing hypotheses & “You assume the cause for skewed bars to be insufficient ground contact due to hollow bottoms, but the motor current trend chart indicates too much grip due to a smeared conveyor belt” \\ \cline{2-3}  & Make it transparent which hypotheses are supported by what information and which have not been tested sufficiently & “The hypothesis of insufficient ground contact as a cause for skewed bars is supported by hollow bottoms, but the motor current trend chart indicates a soiled conveyor belt” \\ \hline \multirow{4}{*}{Positive test strategy} & Present data for situations in which the property of interest differs, thus mitigating the effect of illusory correlations & “Bars had hollow bottoms in 25\% of the situations in which this fault occurred, but also in 18\% of situations without a fault” \\ \cline{2-3}  & Make unequal sample sizes transparent & “There are 311 measurements for milk chocolate but only 17 for caramel chocolate” \\ \cline{2-3}  & Weight evidence by sample size & “The 17 measurements for caramel chocolate may not be representative” \\ \hline \hline \end{tabular}
\end{table}