standard deviation
What It Means
Standard deviation measures how spread out your data points are from the average value. If most of your data clusters tightly around the average, you have a low standard deviation; if data points are scattered widely, you have a high standard deviation. It's essentially a single number that tells you whether your results are consistent and predictable or highly variable.
Why Chief AI Officers Care
Standard deviation is crucial for assessing AI model reliability and performance consistency - models with high standard deviation in their predictions may be unreliable for business decisions. It helps CAIOs identify when AI systems are producing inconsistent results that could lead to operational risks or compliance issues. Understanding this metric is essential for setting appropriate confidence thresholds and determining when human oversight is needed.
Real-World Example
An AI-powered fraud detection system flags transactions with fraud scores averaging 0.65, but the standard deviation is 0.40 - meaning some legitimate transactions score 0.25 while others score 1.05, creating many false positives and negatives. A competing model with the same 0.65 average but only 0.15 standard deviation would be far more reliable for automated decision-making.
Common Confusion
People often confuse standard deviation with simple range (highest minus lowest value) or think a higher standard deviation always means worse performance. Standard deviation specifically measures spread around the average, and in some cases like anomaly detection, higher variability might actually be valuable for identifying outliers.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, standard deviation is critical for quality assurance and clinical decision-making, helping identify when ...
Finance: In finance, standard deviation is the primary measure of investment risk and volatility, quantifying how much an asset's...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"The most widely used measure of dispersion of a frequency distribution introduced by K. Pearson (1893). It is equal to the positive square root of the variance. The standard deviation should not be confused with the root mean square deviation."Source: OECD
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