What is Central Limit Theorem ?
The Central Limit Theorem (CLT) is a fundamental concept in statistics that states that the distribution of the sample means of a population will be approximately normally distributed, regardless of the original distribution of the population, given that the sample size is sufficiently large.
In simpler terms, it suggests that if you take multiple random samples from any population, the average of those samples will tend to follow a normal (bell-shaped) distribution, even if the original population doesn’t have a normal distribution. This theorem is extremely powerful because it allows statisticians to make inferences about population parameters, such as the population mean, based on sample data.
The Central Limit Theorem is widely used in statistical analysis, hypothesis testing, and estimation, as it provides a basis for many statistical techniques and allows researchers to make reliable conclusions about populations based on sample data.
Real-life Applications of Central Limit Theorem
Central Limit Theorem (CLT) is important to several disciplines, including biology, engineering, finance, and more. It clarifies how sample means behaved from any population, irrespective of how dispersed it was, and it shows how remarkably stable and predictable huge samples can be. This article explores the useful uses of the Central Limit Theorem in real-world situations, highlighting its importance.