Statistics — Central Limit Theorem: Reliable Estimation and Informed Decision-Making Through Statistical Inference
The CLT reassures us that if we have a sufficiently large sample, the sample mean will provide a reliable estimate of the population mean. Moreover, by utilizing the standard deviation of the sample means and the sample size, we can estimate the standard deviation of the population. In this essay we investigate this in detail.
The Central Limit Theorem (CLT) is a fundamental concept in statistics that never fails to captivate the mind. At its core, the CLT asserts that regardless of the shape of the original population distribution, if we collect a sufficiently large sample and calculate the mean of that sample, it will follow a normal distribution. (That mean of the sample is Random Variable, as we are selecting the sample from the population randomly). This theorem is truly a game-changer, unlocking a world of possibilities.
It speaks about the Random Variable- “Mean of the Sample”
To understand the power of the CLT, let’s imagine you have a bag filled with marbles of different sizes and colors, and you wish to determine the average size of all the marbles. Now, measuring each and every marble in the bag would be a daunting task, both in terms of time and resources. Instead, you can employ a clever strategy inspired by the CLT.
- By randomly selecting a handful of marbles from the bag, measuring their sizes, and calculating the average, you obtain one sample mean.
- Repeat this process multiple times, and you will have a collection of sample means from different handfuls of marbles.
Now, here’s where the magic unfolds. The CLT assures us that even if the marbles within each handful are not representative of the entire bag, the average of these sample means will serve as a remarkably accurate estimate of the average size of all the marbles. Moreover, as you increase the number of handfuls you measure, your estimate becomes increasingly precise.