The predictability of loss improves when the number of similar units increases according to which law?

The Law of Large Numbers is a fundamental concept in probability and statistics that states that as the number of trials or units increases, the actual ratio of outcomes will converge to the expected ratio of outcomes. This principle is crucial in the insurance industry because it allows insurers to predict losses more accurately when they have a larger pool of similar risks to assess.

In practical terms, when an insurer writes many policies of similar type, the variations in losses due to random occurrences will tend to average out. Therefore, with a larger sample size, the insurer can estimate future claims with greater confidence. This predictability is vital for setting appropriate premiums and ensuring financial stability.

Other options, while related to statistical concepts, do not specifically address the improvement in predictability of loss in the context of increasing similar units of risk. The Law of Averages is a common misconception that does not encapsulate the nuances of statistical convergence. The Law of Risk Assessment and the Law of Statistical Inference, while pertinent to their fields, do not encapsulate the specific statistical principle that governs the predictability of loss in insurance as directly as the Law of Large Numbers does.