Standard Deviation
A measure of how spread out values are from the mean.
Also known as: SD, Sigma, Spread
Category: Concepts
Tags: statistics, measurement, variability, data, analysis
Explanation
Standard deviation measures how spread out values are from the average - the typical distance of observations from the mean. Low standard deviation: values cluster tightly around the mean (consistent, predictable). High standard deviation: values spread widely (variable, less predictable). In normally distributed data: ~68% of values fall within 1 standard deviation of the mean, ~95% within 2, ~99.7% within 3. Practical applications: quality control (variation within tolerance?), risk assessment (how variable are returns?), comparing variability (which process is more consistent?), and identifying outliers (values beyond 2-3 standard deviations are unusual). Related concepts: variance (standard deviation squared), coefficient of variation (standard deviation relative to mean), and range (simpler but cruder measure of spread). Limitations: assumes values are meaningful to average (not always true), sensitive to outliers, and can be misleading in skewed or multimodal distributions. For knowledge workers, standard deviation helps: understand and communicate variability, set realistic expectations (outcomes vary), identify unusual observations, and compare consistency across processes or time periods.
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