normal distribution
normal distribution
When the standard deviation of a population is known, the sampling distribution of the sample mean will be normally distributed, regardless of the shape of the population distribution, due to the Central Limit Theorem. The mean of this sampling distribution will be equal to the population mean, while the standard deviation (known as the standard error) will be the population standard deviation divided by the square root of the sample size. This allows for the construction of confidence intervals and hypothesis testing using z-scores.
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in order to calculate the mean of the sample's mean and also to calculate the standard deviation of the sample's
The standard deviation in a standard normal distribution is 1.
If the samples are drawn frm a normal population, when the population standard deviation is unknown and estimated by the sample standard deviation, the sampling distribution of the sample means follow a t-distribution.
normal distribution
You calculate the standard error using the data.
The answer will depend on the underlying distribution for the variable. You may not simply assume that the distribution is normal.
Thanks to the Central Limit Theorem, the sampling distribution of the mean is Gaussian (normal) whose mean is the population mean and whose standard deviation is the sample standard error.
the standard deviation of the population(sigma)/square root of sampling mean(n)
NO
400
No.
The standard deviation associated with a statistic and its sampling distribution.
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It has the same shape, mean, and standard deviation as the population.