When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
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.
The standard deviation of the population. the standard deviation of the population.
A t-test is performed instead of a z-test when the sample size is small (typically n < 30) and the population standard deviation is unknown. The t-test accounts for the increased variability and uncertainty in small samples by using the sample standard deviation rather than the population standard deviation. Additionally, it is often used when the data is approximately normally distributed.
Yes
True.
When the population standard deviation is known, the sample distribution is a normal distribution if the sample size is sufficiently large, typically due to the Central Limit Theorem. If the sample size is small and the population from which the sample is drawn is normally distributed, the sample distribution will also be normal. In such cases, statistical inference can be performed using z-scores.
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.
The Z test.
A single observation cannot have a sample standard deviation.
The standard deviation of the population. the standard deviation of the population.
A t-test is performed instead of a z-test when the sample size is small (typically n < 30) and the population standard deviation is unknown. The t-test accounts for the increased variability and uncertainty in small samples by using the sample standard deviation rather than the population standard deviation. Additionally, it is often used when the data is approximately normally distributed.
Yes
If the population standard deviation is sigma, then the estimate for the sample standard error for a sample of size n, is s = sigma*sqrt[n/(n-1)]
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.
the sample standard deviation
Standard deviation in statistics refers to how much deviation there is from the average or mean value. Sample deviation refers to the data that was collected from a smaller pool than the population.