What principle ensures that as more similar exposure units are considered, losses align closer to expected probabilities?

The principle that ensures that as more similar exposure units are considered, losses align closer to expected probabilities is the Law of Large Numbers. This statistical theorem states that as the size of a sample increases, the sample mean will converge toward the expected value or the population mean. In the context of insurance, this means that when an insurer collects premiums from a large number of policyholders with similar risks, the actual losses experienced will resemble the predicted losses more closely.

This principle is foundational to the insurance industry because it allows companies to set premiums accurately by estimating future losses based on historical data. The larger the number of exposure units (for example, insured properties or vehicles), the more reliable the estimates of loss probabilities become. Thus, the Law of Large Numbers underpins effective risk pooling, ensuring that the insurer can remain solvent while fulfilling their obligations to policyholders by spreading out risk across a broader base.