Provide a constructive response and avoid critiques to the following paragraph. Considering the dataset's characteristics, I would prefer to use the median (80.5) as the measure of central tendency. The reason is that the presence of extreme values, indicated by the minimum score of 18 and the relatively high standard deviation (19.57), suggests skewness in the data distribution. The median is more robust to outliers and provides a better representation of the data's central tendency in such cases. To determine the thresholds for completion, remediation, and termination, I analyzed the dataset's distribution. For completion, I suggest scores above the third quartile (Q3), which is 87. This indicates that trainees scoring above 87 have demonstrated exceptional product knowledge. For remediation, scores between the median (80.5) and Q3 (87) suggest that trainees have a good foundation but require additional training. Scores below the median but above the first quartile (Q1) of 67.75 may also benefit from remediation. Trainees scoring below Q1 (67.75) or having exceptionally low scores (e.g., below 50) may be considered for termination, as they have struggled significantly with product knowledge. These scores should not be the threshold for all training classes. Each training class may have unique characteristics, such as varying trainee backgrounds, training materials, or instructors. Additionally, the product knowledge test may undergo changes or updates. To ensure fairness and accuracy, it's essential to analyze each training class separately, considering factors like cla