Under what conditions does the Central Limit Theorem hold true?
- When the data is skewed
- When the population is normal
- When the sample size is sufficiently large
- When the standard deviation is zero
The Central Limit Theorem holds true when the sample size is sufficiently large (usually n > 30), regardless of the shape of the population distribution. This theorem states that if you have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed.
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