# Is standard error the same as standard error of the mean?

**Standard Error**is the

**standard deviation**of the sampling distribution of a statistic. Confusingly, the estimate of this quantity is frequently also called "

**standard error**". The [sample]

**mean**is a statistic and therefore its

**standard error**is called the

**Standard Error of the Mean**(SEM).

Simply so, what is the difference between standard error and standard error of the mean?

**Standard Error of the Mean** vs. **Standard Deviation**: The **Difference**. The **standard deviation** (SD) measures the amount of variability, or dispersion, for a subject set of data from the **mean**, while the **standard error of the mean** (SEM) measures how far the sample **mean** of the data is likely to be from the true population **mean**

**Standard Error of Estimate**.

**Definition**: The

**Standard Error of Estimate**is the measure of variation of an observation made around the computed regression line. The smaller the value of a

**standard error of estimate**the closer are the dots to the regression line and better is the

**estimate**based on the equation of the line

Keeping this in consideration, when use standard error of the mean?

The main **use** of the **standard error of the mean** is to give confidence intervals around the estimated means where it follows the same 68-95-99.7 rule BUT this time not for the data itself but for the **mean**. This can also be extended to test (in terms of null hypothesis testing) differences between means.

In statistics, the **standard error** of a sampling statistic indicates the variability of that statistic from sample to sample. Thus, the **standard error** of the mean indicates how much, on average, the mean of a sample deviates from the true mean of the population. The result is the **variance** of the sample.