Difference between Standard Error and Standard Deviation
Difference between Standard Error and Standard Deviation

Difference between Standard Error and Standard Deviation

Many students frequently utilize the standard error when they ought to utilize the standard deviation. When managing numerical information sets, numerous individuals get confounded between the standard deviation of sample and the standard error of sample mean. In this article, we desire to stress on the contrast between these.

Standard Error

The standard error can be written as SE; is standard deviation related to sampling distribution of statistic, most generally of the mean. The term may likewise be utilized to allude to an estimation of that standard deviation, got from a specific sample used to process the estimate.

Standard Deviation

The standard deviation can be written as SD; is a measure which is utilized to evaluate the measure of dispersion or variation of a set. A low standard deviation shows that the information guides incline toward be near the mean while high standard deviation demonstrates that the information focuses are spread out over a more extensive scope of values.

Standard Error VS Standard Deviation

In this article we are going to identify and explain some differences that exist between standard error and standard deviation which help you to understand the contrasts between these two.

  • Measures:

The SEM or standard error of mean, computes how distant the sample mean of information is liable to be from the genuine population mean.

The SD or standard deviation measures the quantity of dispersion or variability for set of information from the mean.

  • Value:

The standard error of mean or SEM is mostly smaller as compare to standard deviation.

The standard deviation is always larger as compare to standard error of mean.

  • Formula:

The formula of standard error of mean (SEM) is the standard deviation by the under root of sample size.

The formula of standard deviation (SD) needs two or three stages. To begin with, take square of difference between every information point and sample mean, finding the aggregate of those values. At that point, divide that aggregate by the sample size less one, which can be said as variance. At last, take the square root of that variance to find out the standard Deviation.

  • Used:

The standard error of mean (SEM) explains how accurate the mean of sample is as compare to the actual mean of population.

The standard deviation explains the volatility as well as can be utilized to measure risk in investment. The assets which have more prices also have higher standard deviation as compare to assets having lower prices. It can also be utilized to find out the significance of price shift in different assets.

  • Sample size:

As the sample size went larger, the standard error of mean gets low as compare to standard deviation. When there is increase in sample size, the actual population mean is identified with higher specificity.

As sample size went larger, the standard deviation gets high as compare to standard error. When there is increase in sample size, it leads to more particular measure of standard deviation. Moreover, it may be less or more usually depending on variability of data added to sample.

Conclusion

From the above article we can conclude that standard error explains accuracy of estimates and standard deviation explains the spread of values.