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Standard error (which is the standard deviation of the distribution of sample means), defined as σ/√n, n being the sample size, decreases as the sample size n increases.
And vice-versa, as the sample size gets smaller, standard error goes up. The law of large numbers applies here, the larger the sample is, the better it will reflect that particular population.
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What affects the standard error of the mean?
The standard error of the underlying distribution, the method of selecting the sample from which the mean is derived, the size of the sample.
How does sample size affect the size of your standard error?
The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.
How does one calculate the standard error of the sample mean?
Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.
What happens to the standard error of the mean if the sample size is decreased?
The standard error increases.
What is the value of the standard error of the sample mean?
The sample standard deviation (s) divided by the square root of the number of observations in the sample (n).
