The central limit theorem states that the sampling distribution of a sample mean is approximately normal if the sample size is large enough,

*even if the population distribution is not normal.*The central limit theorem also states that the sampling distribution will have the following properties:**1.**The

**mean**of the sampling distribution will be equal to the mean of population distribution:

x = μ

**2.**The

**standard deviation**of the sampling distribution will be equal to the variance of the population distribution divided by the sample size:

s = σ / √n

To find the sample mean and sample standard deviation of a given sample, simply enter the necessary values below and then click the “Calculate” button.

Sample mean (x) = **17**

Sample standard deviation (s) = **0.8**