Statistically Significant vs. Practically Efficient Effects In my research, I stress the need of differentiating between statistically significant and practically efficient effects when assessing Amazon's training programs. Statistically significant impacts are results that are unlikely to have occurred by chance alone, and I usually assess them using statistical tests. These findings provide support to the success of the training programs and encourage more investment in similar projects. In contrast, I realize that practically efficient effects relate to outcomes that provide limited or insufficient advantages in everyday situations. It is critical for Amazon to evaluate the practical implications of a program, including whether the advantages of enhanced performance outweigh the cost of training. I've seen that businesses often use ROI (Return on Investment) to judge if training was worthwhile. I underline the importance of these measures in assessing Amazon's training implementation and assessment. While statistical evaluations confirm that improvements are real, actual performance sheds light on the economic sustainability of continuing these types of efforts. To maximize training results, I propose that Amazon use statistical analysis to verify the benefits of training while also doing costs and benefits evaluations to guarantee that these kinds of impacts are beneficial in practice. Find an in-text citation and reference