The statistical fact that it becomes harder and harder to maintain a given level...
A mathematical principle of probability stating that the actual losses in a given category of insurance will come closer to a predictable number as the number of units of exposure increases. In insurance, a prediction must be made from actuarial experience or statistical analysis of the number of losses to be expected in a group of exposures. (The larger the sample, the more accurate the prediction.)
The mean of a random sample approaches the mean (expected value) of the population as sample size increases.
A principle governing the development of statistics that the greater quantity of examples used will increase the reliability of the findings.
A principle that the larger the number of exposures considered, the more closely will reported losses equal the true probability of loss. This is the basis for the statistical expectation of loss, which determines premium rates.
A concept in probability and statistics that the larger the number of units exposed to loss, the greater the ability to predict loss results accurately.
The theory of probability which specifies that the greater the number of observations made of a particular event, the more likely it will be that the observed results produce an estimate of the probability of the event occurring.
A component of the law of probabilities that states that the larger the size of the group, the more accurately the loss experience of that group can be predicted. This is one of the main principles involved in determining actuarial tables for insurance purposes.
The theory of probability which specifies that the greater the number of observations made of a particular event, the more likely it will be that the observed results will approximate the results anticipated by the mathematics of probability.----------[ Back
A mathematical law stating that if a large number of similar persons or objects are exposed to the same risk, a predictable number of losses will occur during a given period of time.
a statistical principle about the properties of large numbers. "The larger the number of chance-determined repetitious events considered, the closer the alternatives will approach predictable ratios."
(statistics) law stating that a large number of items taken at random from a population will (on the average) have the population statistics
If the number of trials of an experiment is large, then the outcomes' experimental probabilities will be close to the outcomes' theoretical probabilities.
A principal which states that the more examples used to develop any statistic, the more reliable the statistic will be.
Principle that states that the more examples used to develop any statistic, the more reliable the statistic will be.
An underlying principle of insurance; the larger the number of participants in a given arrangement, the more accurate the rate is to the exposure.
A large insurance portfolio enables the actuary to predict better the number of claims. The principle reduces the number of random fluctuations of claims as the number of lives insured slowly grows. There is substantial decline in standard deviation of claims arising from pure chance with increase in number of insured.
The greater the number of exposures, the more nearly wifi the actual results obtained approach the probable results expected with an infinite number of exposures. As a principle in mathematics, the law of large numbers has several specific requirements for its application, requirements which are never met. It does not state what will happen; it states only what will probably happen. When operating efficiently, the law allows the insurer to predict its total claims with manageable accuracy.
A mathematical concept which postulates that the more times an event is repeated (in insurance, the larger the number of homogeneous exposure units), the more predictable the outcome becomes. In a classic example, the more times one flips a coin, the more likely that the results will be 50% heads, 50% tails.
A mathematical premise in which numerous similar units simultaneously exposed to similar hazards greatly increase the ability to predict likely outcomes accurately.
A natural law of probability which states that the larger the number of exposures to risk of independent, homogeneous units the closer will be the actual number of casualties to the probable number in an infinite series.
(Loi des grands nombres) A mathematical principle of probability in which the larger the number of risks or exposures used in the calculation, the more accurate the total (as distinct from the individual) result will be.
One of several theorems expressing the idea that as the number of trials of a random process increases, the percentage difference between the expected and actual result values goes to zero.
While it is impossible to prdict either the time or the loss amount of adverse events in relation to individuals, the averages for a sufficiently large set (of insureds) exhibit certain patterns of loss frequency and loss extent
The theory of probability on which the business of insurance is based. Simply put, this mathematical premise says that the larger the group of units insured, such as sport-utility vehicles, the more accurate the predictions of loss will be.
The mean of a random sample approaches the mean ( expected value) of the population as the sample grows.
in statistics, the larger a sample size, the more closely it will coincide with the statistics of the population as a whole - e.g., the more times you flip a fair coin, the more closely the results will approach 50-50 heads-tails. In insurance, the larger the number of exposures, the more closely the amount of losses will match those expected based on population statistics (assuming an adequate spread of risks), thus the more predictable the company's underwriting results, which is a fundamental basis of insurance. (See ACTUARY)
Concept that the greater the number of exposures (for example, lives insured), the more closely will actual results approach the results expected from an infinite number of exposures. Thus, the larger the number of people in the insured risk pool, the more stabile the likely results of risk event occurrences.
This law states that the larger the number of exposures considered, the more closely the losses reported will match the underlying probability of loss. The simplest example of this law is the flipping of a coin. The more times the coin is flipped, the closer it will come to actually reaching the underlying probability of 50% heads and 50% tails. See also Degree of Risk, Odds, and Probability.
A concept that the greater number of exposures, the more closely actual results approach the probable results expected from a number of exposures.
The law of large numbers is a fundamental concept in statistics and probability that describes how the average of a randomly selected large sample from a population is likely to be close to the average of the whole population. The term "law of large numbers" was introduced by S.D.