The law of large numbers allows an insurer to predict losses accurately when a large number of similar, relatively independent exposures are pooled. Which event would MOST undermine the insurer's reliance on this principle?
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Answer & full 3-part explanation (select an option above, or peek)
Why A is correct
The law of large numbers works when losses are random and exposures are sufficiently independent so that actual losses converge toward expected losses. A pandemic creates correlated losses: many policyholders fall ill at the same time from the same cause, so the independence assumption breaks down and actual claims can deviate massively from predictions. Insurers manage this risk with reinsurance, geographic diversification, and catastrophe modeling. Adding similar policyholders, trimming benefits, or standardizing the questionnaire does not break the statistical assumptions.
Why the other options are wrong
- B) Adding similar, independent exposures increases the pool size and makes loss prediction MORE reliable, not less.
- C) Reducing covered benefits reduces claim exposure; it does not undermine the statistical predictability of the remaining risks.
- D) A uniform health questionnaire helps classify risks consistently; it does not destroy the independence of the exposures.
Memory hook
One big shared cause floods the pool. Independence is the law's secret ingredient.