The Law of Large Numbers is a statistical theorem that states that as the number of trials or observations increases, the average of the results will tend to converge on the expected value. The more times you repeat an experiment, the closer the average of the outcomes will be to the theoretical probability.
- Convergence to expected value: As the sample size grows, the sample average approaches the population average.
- Reduced variability: With more data points, the results become less variable and more predictable.
- Foundation for statistical inference: It’s a fundamental principle in statistics, used to estimate population parameters from sample data.
The Law of Large Numbers is a cornerstone of probability and statistics, providing a foundation for understanding how random events behave in the long run.
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