What effect does increasing the number of individuals have on predicting losses?

Increasing the number of individuals in a dataset leads to a decrease in the range of error when predicting losses. This principle is grounded in statistical analysis and risk theory, often referred to as the law of large numbers. As more observations are included in the analysis, the sample becomes more representative of the overall population.

With a larger sample size, the estimates of loss predictions become more stable and precise, leading to a narrower range of error in those predictions. This enhanced accuracy results from the ability to better capture the underlying patterns and reduce the impact of random fluctuations that may occur in smaller samples. In practical terms, when a bail bondsman has data on more cases, they can analyze trends and make predictions about potential losses with greater confidence and reliability. Thus, increasing the number of individuals directly improves the predictability and accuracy of loss predictions in the bail bond industry.