The law of large numbers states that as the number of trials in a random experiment increases, the average of the results obtained from the experiment approaches the expected value. In statistics, it's used to describe the relationship between sample size and the accuracy of statistical estimates.
In data science, the law of large numbers is used to understand the behavior of random variables over many trials. It's often applied in areas such as predictive modeling, risk assessment, and quality control to ensure that data-driven decisions are based on a robust and accurate representation of the underlying patterns in the data.
The law of large numbers helps to guarantee that the average of the results from a large number of independent and identically distributed trials will converge to the expected value, providing a foundation for statistical inference and hypothesis testing.
