According to the law of large numbers, how does the number of similar units influence loss predictability?

The law of large numbers is a fundamental concept in statistics and insurance, which states that as the number of similar units increases, for example, in the context of health or life insurance policies, the actual outcomes will begin to converge towards the expected outcomes. This means that with a larger sample size, the variability of losses becomes lower, making predictions about future losses more accurate.

When a large number of similar units, such as policyholders with comparable risk profiles, is considered, the randomness of individual outcomes is minimized. This leads to a more stable and predictable average of losses. Insurers can better estimate how many claims they are likely to face and calculate premiums accordingly, which ultimately improves their financial stability.

In contrast, if there are only a few similar units, the variability and unexpected outcomes can significantly deviate from expected norms, leading to unpredictability in loss estimation. Therefore, having a larger group enhances the ability to predict losses accurately, which is why improving predictability is the correct understanding of the law of large numbers in this context.