number1 - Required. First number, cell reference, or range to calculate standard deviation (e.g., A1:A100).
number2 - Optional. Optional. Additional numbers or ranges. Up to 255 arguments allowed.
| A | B | C | |
|---|---|---|---|
| 1 | Sample | Weight (g) | Metrics |
| 2 | Sample 1 | 500.2 | |
| 3 | Sample 2 | 499.8 | |
| 4 | Sample 3 | 500.5 | |
| 5 | Sample 4 | 500 | |
| 6 | Sample 5 | 499.9 | |
| 7 | =STDEV.S(B2:B6) 0.27 |
Use when data is a sample from larger population
Testing 100 products from 10,000 production line → 15.3
Use when data includes entire population
All 30 students in class test scores → 12.7
Combine average with standard deviation
Display as "500.1 ± 0.38" for quality reporting
Calculate upper control limit (±2σ = 95%)
Average + 2×StdDev → 500.86 (upper limit)
Manufacturing facilities use the standard deviation function in Excel to measure product consistency and maintain quality standards. Lower standard deviation indicates tighter quality control and more consistent manufacturing processes. The STDEV.S standard deviation function helps quality assurance teams identify production issues early, set acceptable tolerance ranges, and demonstrate compliance with industry standards. This function enables manufacturers to optimize processes, reduce waste from out-of-spec products, and maintain customer satisfaction through consistent product quality measurement.
| A | B | C | |
|---|---|---|---|
| 1 | Sample | Weight (g) | Status |
| 2 | Sample 1 | 500.2 | |
| 3 | Sample 2 | 499.8 | |
| 4 | Sample 3 | 500.5 | |
| 5 | Sample 4 | 500 | |
| 6 | Sample 5 | 499.9 | |
| 7 | Sample 6 | 500.3 | |
| 8 | Sample 7 | 500 | |
| 9 | Average | 500.1 | |
| 10 | Std Dev | 0.24 | =STDEV.S(B2:B8) Excellent |
Financial analysts apply the standard deviation function in Excel to measure investment risk and portfolio volatility. Higher standard deviation indicates greater price fluctuations and investment risk. This function helps investors compare risk levels between different assets, build balanced portfolios with appropriate risk profiles, and make informed decisions about asset allocation. The standard deviation function in Excel enables financial professionals to quantify uncertainty, set realistic return expectations, and communicate risk to clients using industry-standard metrics.
| A | B | C | |
|---|---|---|---|
| 1 | Month | Return % | Analysis |
| 2 | Jan | 5.2 | |
| 3 | Feb | -2.1 | |
| 4 | Mar | 8.5 | |
| 5 | Apr | 3.3 | |
| 6 | May | -1.5 | |
| 7 | Jun | 6.8 | |
| 8 | Avg Return | 3.4 | |
| 9 | Risk (StdDev) | 4.2 | =STDEV.S(B2:B7) Moderate |
❌ The Problem:
✅ Solution:
=STDEV.S(A1:A5)Use range notation (A1:A5) instead of listing individual cells. The standard deviation function in Excel efficiently processes ranges of any size. For large datasets spanning hundreds or thousands of rows, range notation makes formulas maintainable and reduces errors. Range references automatically expand when you insert new data rows, ensuring your standard deviation function calculation stays accurate as data grows.
❌ The Problem:
✅ Solution:
=STDEV.S(sample_data)Use STDEV.S when analyzing sample data from a larger population. The STDEV.S standard deviation function applies Bessel's correction (dividing by n-1 instead of n) to provide an unbiased estimate of population standard deviation. STDEV.P is only appropriate when you have the complete population, such as analyzing all students in a single class or all transactions for a completed period. When in doubt, use STDEV.S - it's the correct choice for most real-world statistical analysis scenarios in Excel.
❌ The Problem:
✅ Solution:
=STDEV.S(IF(ABS(A1:A100-AVERAGE(A1:A100))<3*STDEV.S(A1:A100),A1:A100))Consider removing or investigating outliers before calculating standard deviation in Excel. Outliers can significantly distort your results, making your data appear more variable than it actually is. Use the standard deviation function in Excel with conditional logic or data filters to exclude outliers beyond 3 standard deviations from the mean. Always document outlier treatment decisions and investigate root causes - outliers often reveal important data quality issues or special circumstances requiring attention.
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