What Is Central Limit Theorem (CLT)?
Central Limit Theorem (CLT) Definition
The Central Limit Theorem (CLT) is a statistical theory that posits that the mean and standard deviation derived from a sample, will accurately approximate the mean and standard deviation of the population the sample was taken from as the size of the sample increases. The minimum number of members of a population needed in order for a sample to adequately represent the population it was pulled from, is 30 according to the central limit theorem.
Defining CLT in Simple Terms
To define CLT in another way, let’s imagine that a sample of 30 stock analysts were gathered together and asked how much they thought a certain stock was going to rise in the next quarter. If the average answer from the sampled analysts was 5%, then according to the CLT, this answer would reasonably approximate the answer of every person working as a stock analyst.
Purpose of the Central Limit Theorem
In finance, the central limit theorem can be used to expedite analysis. Since indices often have hundreds, sometimes thousands of stocks contained within them an analyst doesn’t have enough time in a month, much less a day to go through them all. But by putting the CLT to work, an analyst can take just 30 stocks out of an index and be able to approximate the quality of the index as a whole and thereby make a confident assessment.
What Is CLT (Central Limit Theorem) FAQs
About the Author
True Tamplin, BSc, CEPF®
True Tamplin is a published author, public speaker, CEO of UpDigital, and founder of Finance Strategists.
True is a Certified Educator in Personal Finance (CEPF®), author of The Handy Financial Ratios Guide, a member of the Society for Advancing Business Editing and Writing, contributes to his financial education site, Finance Strategists, and has spoken to various financial communities such as the CFA Institute, as well as university students like his Alma mater, Biola University, where he received a bachelor of science in business and data analytics.