AI engineering glossary · Probability
What is CLT (central limit theorem)?
Central limit theorem (also called CLT): A theorem stating that the average (or sum) of many independent observations with finite variance is approximately normally distributed, whatever the shape of the original distribution. The spread of that average shrinks as sigma / square root of n.
Central limit theorem: an example
Individual order values may be very skewed, yet the average of 100 orders is roughly bell-shaped, letting us build confidence intervals.
Related terms
More Probability terms
- Chain rule of probability
- Chebyshev's inequality
- Classical probability
- Cluster sampling
- Combination
- Complement rule
- Conditional distribution
- Conditional expectation
Where you learn it
Central limit theorem is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.