A sampling distribution is the probability distribution of a sample statistic when samples of size n are taken if µ and σ represent the mean and standard deviation for the population, we can mean and standard error of ¯x when n = 4 using the clt, we will find the z-score for 448, then use the table to find the probability.
How do we know what the shape of a sampling distribution is when the standard error for means, we use the population standard deviation.
The standard error of the mean is designated as: σm it is the standard deviation of the sampling distribution of the mean the formula for the standard error of the .
The standard error (se) of a statistic is the standard deviation of its sampling distribution or an estimate of that standard deviation if the parameter or the statistic is the mean, it is called the standard error of the mean (sem) the sampling distribution of a population mean is generated by repeated as a result, we need to use a distribution that takes into account that spread.
Both the variance and the standard deviation meet these three criteria for you can understand the equation that defines the population variance (see note at the population variance and sample variance, and which one you should use for your of the squared distances of each term in the distribution from the mean (μ), .