Watch how sampling distributions become normal regardless of population shape
Population Distribution
Sampling Parameters
Population Distribution
Sampling Distribution of Means
Statistics
Understanding the Central Limit Theorem
The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution shape.
Key Points:
The mean of the sampling distribution equals the population mean
The standard deviation of the sampling distribution (standard error) equals σ/√n
As n increases, the sampling distribution becomes more normal
This allows us to make inferences about populations using normal distribution theory