Understanding the Central Limit Theorem
The Central Limit Theorem (CLT) is a fundamental principle in statistics. It states that when you have a sufficiently large sample size, the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution. 🐸
This holds true as the sample size (n) becomes large, typically n > 30 is considered large enough.
Applications of CLT
- Helps in constructing confidence intervals for estimates of population parameters.
- Facilitates hypothesis testing.
- Used in various statistical and machine learning models.
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