Standard Deviations in Sampling
Standard deviations in sampling are known as standard error, which is a measure of the variation or dispersion of a sampling distribution. It represents how spread out the sample means are from the true population mean.

Explanation:

Definition:
- Standard error is the standard deviation of the sampling distribution of a statistic, such as the mean. It measures the accuracy with which a sample represents a population.

Role:
- Standard error is crucial in inferential statistics as it helps in estimating the precision of sample statistics. It indicates how much the sample mean is likely to vary from the population mean.

Calculation:
- Standard error is calculated by dividing the standard deviation of the population by the square root of the sample size. It is denoted by the symbol "SE".

Interpretation:
- A smaller standard error indicates that the sample mean is likely to be close to the population mean, while a larger standard error suggests greater variability.

Significance:
- Standard error is used to calculate confidence intervals and determine the margin of error in hypothesis testing. It is essential for making accurate inferences about the population based on sample data.
In summary, standard deviations in sampling are referred to as standard error, which plays a significant role in estimating the precision of sample statistics and making valid statistical inferences about the population.