Which concept would provide consistency in risk prediction across a large group of cases?

The Law of Large Numbers is a fundamental principle in probability and statistics that states that as the number of trials or cases increases, the average of the results will converge to the expected value. In the context of risk prediction, this concept ensures that the variation in outcomes decreases as the size of the sample group grows. Therefore, using this principle allows insurers and analysts to make more accurate predictions about risk based on a larger pool of data.

When evaluating a large group of cases, the Law of Large Numbers helps in stabilizing the predicted risk, as it minimizes anomalies and provides a clearer picture of trends and averages within that population. This consistency is essential for insurers to manage their portfolios effectively, set premiums, and provide accurate assessments of risk.

While statistical analysis, risk management, and policy review are all crucial components of evaluating and understanding risk, they do not primarily focus on the consistency derived from large sample sizes like the Law of Large Numbers does. This law fundamentally underpins the rationale behind many statistical methods used in risk assessment and is critical for achieving reliable predictions over time.