Standard deviation is a measure of variation in data. It allows comparison between two or more sets of data to determine if their averages are truly different. For example, if the average salaries in two companies are $90,000 and $70,000 with a standard deviation of $20,000, the difference in average salaries between the two companies is not statistically significant.
Standard deviation measures the dispersion of a given data set. It indicates how close to the average the data is clustered. It can be used to measure the confidence in statistical data. For example, for a data set of 2, 6, 10, 14 and 18, the average of 10 is less reliable than the average of 10 for the data set of 8, 9, 10, 11 and 12, because the data in the first set is more dispersed (more variability) than the data in the second set. Standard deviation is used to compare different sets of data. For example, in science, standard deviation is used to test two sets of data to measure the confidence in the difference observed in two or more sets of data. In predicting weather patterns, standard deviation can tell the variation in maximum and minimum temperatures for two different cities. For example, cities A and B might have the same average temperature of 70 degrees, but city A may have a maximum temperature of 100 degrees and a minimum of 40 degrees (a variation of 30 degrees from the average) while city B may have a lower standard deviation with a maximum temperature of 80 degrees and a minimum of 60 degrees (a variation of only 10 degrees from the average).