What bias arises when training data excludes participants who have dropped out over time?

The correct bias that arises when training data excludes participants who have dropped out over time is Survivorship Bias. This type of bias occurs when only those subjects or data points that have "survived" a particular process are included in the analysis, leading to inaccuracies and an overestimation of the conclusions drawn.

Survivorship Bias can significantly skew results because it fails to consider the characteristics and data of those who did not persist or were not observed until the end of a study or process. In this context, if data only reflects individuals who remained active participants, it may lead to misconceptions about the overall population or the effectiveness of treatments or interventions.

In contrast, Attribution Bias, Selection Bias, and Confirmation Bias refer to different aspects of data analysis and interpretation processes. Attribution Bias deals with incorrectly linking outcomes to specific causes, Selection Bias pertains to improper sampling methods where certain groups are disproportionately represented, and Confirmation Bias involves the tendency to favor information that confirms pre-existing beliefs. Each of these biases operates under different circumstances and does not specifically relate to the scenario of excluding participants who drop out over time.