How is volatility commonly measured in datasets?

Volatility in datasets, particularly in finance and investments, is commonly quantified through a statistical measure known as standard deviation, which is directly related to variance. To calculate the standard deviation, you first compute the variance, which involves finding the average of the squared deviations from the mean. The variance is then square-rooted to scale it back to the original units of the data, providing a measure of the dispersion or spread of the data points around the mean.

By opting for the calculation of variance and taking the square root, you obtain a clear representation of volatility, which reflects how much the data points (such as asset returns) deviate from the average return. High volatility indicates greater variability in returns, signaling increased risk, while low volatility suggests more stable returns. Thus, this method is fundamental in risk assessment and portfolio management, making it essential for anyone involved in investments to understand this concept thoroughly.