What principle states that larger groups of insureds lead to better prediction of losses?

The principle that states that larger groups of insureds lead to better prediction of losses is known as the Law of Large Numbers. This principle is fundamental in insurance and risk management because it posits that as the size of a sample increases, the more accurately the actual loss experience will reflect the expected loss. This is crucial for insurers, as they rely on statistical models to calculate premiums and ensure that they can cover the losses incurred.

When a larger group is insured, the variability of individual losses is smoothed out. Individual events are often unpredictable, but when aggregated across many policyholders, the overall loss becomes more predictable, enabling insurers to set premiums more accurately, align reserves appropriately, and maintain financial stability. By spreading out the risk among a large population, insurers can also manage and absorb fluctuations in loss experience more effectively.

The other principles mentioned do not relate to the aggregation of data for loss prediction in the same way. For example, the Principle of Risk Transfer focuses on shifting the liability from one party to another, and the Principle of Insurable Interest relates to the need for an insured to have a legitimate interest in the insured item to prevent moral hazard. The Law of Risk Evaluation does not specifically address the aggregation of insured groups and their impact on loss