Central Limit Theorem (CLT) Visualizer

Understand the power of the Central Limit Theorem by simulating sample means and observing their distribution.

Population Distribution

Population Mean

Population Standard Deviation

Sample Size

Number of Samples

Distribution of Sample Means

About Central Limit Theorem (CLT)

The Central Limit Theorem (CLT) is a fundamental concept in statistics. It states that the distribution of the sample means of a sufficiently large number of independent and identically distributed random variables, regardless of the original distribution's shape, will approximate a normal distribution. This theorem is crucial because it allows us to make inferences about the population mean based on sample means, even when we don't know the population's distribution.

This tool visually demonstrates the CLT. By choosing different population distributions and sample parameters, you can observe how the distribution of sample means tends towards a normal distribution as the number of samples increases. Experiment with different settings to deepen your understanding of this powerful theorem.