Which principle states that larger numbers of similar risks lead to more reliable predictions of losses?

The principle that larger numbers of similar risks lead to more reliable predictions of losses is known as the Law of Large Numbers. This principle is fundamental in insurance and statistical theory as it posits that as the size of a sample increases, the average of the sample becomes a more accurate reflection of the expected value or population average. In practical terms, it means that when an insurance company insures many similar risks, it can predict overall losses more accurately, which is essential for setting premiums and maintaining financial stability.

In the context of the insurance industry, this principle allows insurers to pool a large number of similar insurance policies to ensure that the unpredictability of individual claims is balanced out by the total number of claims. This reduces risk and helps to stabilize the insurer's financial expectations.

The other concepts listed do not encapsulate this principle. The Law of Averages refers more broadly to the expectation that outcomes will even out over time without the statistical underpinning that the Law of Large Numbers provides. The Principle of Risk Assessment more generally addresses the evaluation of potential losses rather than the prediction accuracy based on sample size. The Law of Probability focuses on the likelihood of specific events occurring, but does not specifically address the aggregation of risks. Thus, the Law of Large Numbers is