mathematical premise stating that the greater the number of exposures: (1) the more accurate the prediction; (2) the less the deviation of the actual losses from the expected losses (X - x approaches zero); and (3) the greater the credibility of the prediction (credibility approaches one). This law forms the basis for the statistical expectation of loss upon which premium rates for insurance policies are calculated.
statistical concept holding that the greater the number of units in a projection, the less important each unit becomes. Group insurance, which gets cheaper as the group gets larger, is an example of the principle in application; actuarial abnormalities have less influence on total claims.
mathematical premise stating that the greater the number of exposures, (1) the more accurate the prediction; (2) the less the deviation of the actual losses from the expected losses (X - x approaches zero); and (3) the greater the credibility of the prediction (credibility approaches 1). This law forms the basis for the statistical expectation of loss upon which premium rates for insurance policies are calculated. Out of a large group of policyholders the insurance company can fairly accurately predict not by name but by number, the number of policyholders who will suffer a loss. Life insurance premiums are loaded for the expected loss plus modest deviations. For example, if a life insurance company expects (x) 10,000 of its policyholders to die in a particular year and that number or fewer actually die (X), there is no cause for concern on the part of the company's actuaries. However, if the life insurance company expects (x) 10,000 of its policyholders to die in a particular year and more than that number dies (X) there is much cause for concern by actuaries.
