What principle explains that the larger the number of units independently exposed to loss, the more accurate the prediction of loss results?

The principle that accurately describes the relationship between the number of units exposed to risk and the predictability of loss outcomes is known as the Law of Large Numbers. This principle holds that as the number of exposure units increases, the actual loss experience will converge closer to the expected loss based on statistical probabilities.

In risk management and insurance, this concept is crucial as it allows insurers to make reliable predictions about future losses by relying on a larger dataset. For instance, if an insurer is evaluating potential losses for a pool of insured individuals, as more individuals are added to that pool, the insurer can be more confident in projecting overall losses based on historical data. This principle is foundational in actuarial science, helping insurers set appropriate premiums and manage risk effectively.

The other choices do not accurately capture this concept. The Law of Averages pertains more generally to expectations of likelihood and outcomes over time but lacks the specific statistical grounding found in the Law of Large Numbers. The Law of Regression relates to the tendency of extreme data points to move closer to the mean over time, which does not directly address predictability based on exposure units. The Law of Variability discusses fluctuations in data and outcomes but does not specifically emphasize the role of large datasets in enhancing the accuracy of predictions.