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The standard deviation (SD) is a single number that summarizes the variability in a dataset. It represents the typical distance between each data point and the mean. Smaller values indicate that the data points cluster closer to the mean—the values in the dataset are relatively consistent.
The standard deviation tells you how spread out from the center of the distribution your data is on average. Many scientific variables follow normal distributions, including height, standardized test scores, or job satisfaction ratings.
Standard deviation measures the spread of a data distribution. The more spread out a data distribution is, the greater its standard deviation. For example, the blue distribution on bottom has a greater standard deviation (SD) than the green distribution on top:
Standard deviation is the measure of the dispersion of the statistical data. Learn the definition of standard deviation and variance, formulas along with the solved examples.
The Standard Deviation is a measure of how spread out numbers are. You might like to read this simpler page on Standard Deviation first. But here we explain the formulas. The symbol for Standard Deviation is σ (the Greek letter sigma). This is the formula for Standard Deviation: Say what? Please explain! OK. Let us explain it step by step.
The Standard Deviation is a measure of how spread out numbers are. Its symbol is σ (the greek letter sigma) The formula is easy: it is the square root of the Variance.
Standard deviation is a statistic measuring the dispersion of a dataset relative to its mean. It is calculated as the square root of the variance. Learn how it's used.
Standard deviation explained in plain English. How to find it by hand or using technology. Standard deviation symbol. Step by step examples.
Standard deviation is a statistical measure of variability that indicates the average amount that a set of numbers deviates from their mean. The higher the standard deviation, the more spread out the values, while a lower standard deviation indicates that the values tend to be close to the mean.
Basically, a small standard deviation means that the values in a statistical data set are close to the mean (or average) of the data set, and a large standard deviation means that the values in the data set are farther away from the mean.