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State the Central Limit Theorem in one sentence.

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Answer

  • For independent, identically distributed samples with finite mean mu and variance σ2\sigma^{2}, the sampling distribution of the mean approaches a normal distribution N(μ,  σ2  /  n)N(\mu, \;\sigma^{2}\; / \;n) as n grows, regardless of the underlying distribution.
Check yourself — multiple choice
  • CLT says all data becomes normal
  • CLT applies only to normal populations
  • The sample mean of i.i.d. draws converges to a normal distribution
  • CLT requires infinite variance

CLT is about the distribution of the sample mean, not of the raw data.

#clt#probability

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