What mathematical principle states that increased exposure units make loss predictions easier?

The chosen answer, the Law of Large Numbers, is fundamental in statistics and probability theory, particularly in the context of risk assessment and insurance. This principle states that as the number of trials or exposure units increases, the average of the results obtained from those trials is more likely to converge on the expected value (or mean).

In practical terms, this means that with a larger pool of data, such as more policyholders in an insurance context, actuaries can make more reliable and accurate predictions about future losses. The variability of individual outcomes decreases as the number of observations increases, which ultimately improves the predictability of losses. This principle is essential for insurance companies because it allows them to set premiums based on expected losses, thereby ensuring profitability while adequately covering claims.

The other options, while relevant to statistics and probability, do not specifically address the relationship between increased exposure units and the accuracy of loss predictions in the same way the Law of Large Numbers does. The Central Limit Theorem relates to the distribution of sample means, probability distribution describes the likelihood of various outcomes, and statistical independence refers to the lack of correlation between events. None of these principles directly addresses the enhancement of predictive accuracy through larger datasets like the Law of Large Numbers does.