Standard Deviation Rule Calculator

Standard Deviation Rule Calculator calculator can be used to calculate the mean, standard deviation, and probabilities using the 68-95-99.7 rule (Empirical Rule).

Input Parameters

Calculation Results

68-95-99.7 Rule Probabilities

Within 1 standard deviation (μ ± σ): 68%

Within 2 standard deviations (μ ± 2σ): 95%

Within 3 standard deviations (μ ± 3σ): 99.7%

Where:
μ = Mean
σ = Standard Deviation
The 68-95-99.7 rule states that for a normal distribution:
- Approximately 68% of data falls within 1 standard deviation of the mean
- Approximately 95% of data falls within 2 standard deviations of the mean
- Approximately 99.7% of data falls within 3 standard deviations of the mean

Standard Deviation Rule Calculator Usage Guide

Learn how to use the Standard Deviation Rule Calculator and understand the 68-95-99.7 rule

What is the Standard Deviation Rule (68-95-99.7 Rule)?

The Standard Deviation Rule, also known as the Empirical Rule, is a statistical concept that applies to normal distributions. It states:

  • Approximately 68% of data falls within 1 standard deviation of the mean (μ ± σ)
  • Approximately 95% of data falls within 2 standard deviations of the mean (μ ± 2σ)
  • Approximately 99.7% of data falls within 3 standard deviations of the mean (μ ± 3σ)

How to Use This Calculator

  1. Enter the mean (μ) of your dataset
  2. Enter the standard deviation (σ) of your dataset
  3. Click the "Calculate" button to see the probabilities

Example Usage

Suppose you have a dataset with a mean of 100 and a standard deviation of 15:

  • You would expect approximately 68% of values to fall between 85 (100-15) and 115 (100+15)
  • Approximately 95% of values to fall between 70 (100-2×15) and 130 (100+2×15)
  • Approximately 99.7% of values to fall between 55 (100-3×15) and 145 (100+3×15)

Practical Applications

This rule is widely used in statistics, quality control, finance, and many other fields to understand data distributions and make predictions. It helps in:

  • Identifying outliers in data
  • Understanding the spread of measurements
  • Making predictions about future observations
  • Setting control limits in quality control