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Generate random numbers with a specific margin of error. Ideal for testing, simulation, and data analysis.
Random Range: 95.00 ~ 105.00
Error: ±5%
Click "Generate" to get a random value
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Random Email Generator
Bulk generate random email addresses for testing and sign-ups with custom prefixes and domains.
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Generate random phone numbers by country, carrier, and prefix on demand for development, testing, and data simulation.
SHAKE Hash Generator
Variable-length hash generator for SHAKE-128/256, featuring salt support, multiple iterations, and various input/output formats.
Chinese D-Series Paper Size Lookup
Look up exact millimeter dimensions for D0 to D6 series paper based on the GB/T 19999-2003 national standard, suitable for book, magazine, and office paper design.
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When you need to simulate measurement errors or test boundary conditions in real-world data, traditional random number generators often lack a controllable margin of error. This tool generates random numbers with a specified variance percentage, where each value fluctuates within the target value ± the margin of error. These variance-based random numbers form a sequence that floats up and down around a central target value based on your set percentage. This is commonly used in scenarios like sensor simulation and quality control testing.
How is the margin of error percentage calculated?
Direct answer: Error Range = Target Value × (1 ± Margin of Error Percentage / 100). For example, with a target value of 100 and a 5% margin of error, the results will fluctuate between 95 and 105.
Why is there a limit on decimal places?
Exceeding 8 decimal places can lead to floating-point precision issues, and most practical applications do not require excessively high precision. Up to 8 decimal places is more than sufficient for the vast majority of engineering and scientific research needs.
We recommend setting the margin of error percentage between 0-100%; setting it too high may cause data distortion. When generating large amounts of data, please be aware of browser performance limitations. We suggest generating no more than 10,000 values per request.
When simulating sensor data, we recommend setting the margin of error based on the equipment's specification sheet. Typical example: If a temperature sensor has a nominal error of ±2% and the target value is 25°C, the random numbers generated by the tool will fluctuate between 24.5°C and 25.5°C (with 1 decimal place). Note that error distribution in real-world environments may be non-linear; this tool defaults to a uniform distribution.
