The 68-95-99.7 rule is a shorthand used to describe the characteristics of a normal distribution (also called a Gaussian distribution). It applies to data sets that follow this bell-shaped curve, where most of the data points are concentrated around the mean, with fewer data points as you move away from the mean.

The rule breaks down as follows:

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

When Does It Apply?

The 68-95-99.7 rule applies only to normal distributions, where the data forms a symmetric, bell-shaped curve around the mean. This rule is important in statistics, as it helps to estimate the likelihood of data falling within a certain range.

Key Points:

• 68% of the data falls between μ - σ and μ + σ.
• 95% of the data falls between μ - 2σ and μ + 2σ.
• 99.7% of the data falls between μ - 3σ and μ + 3σ.

This rule is useful in many fields, including finance, engineering, and social sciences, whenever you're dealing with normally distributed data.

Would you like to go into more detail or ask further questions?

5 Related Questions:

1. How do you calculate the standard deviation for a normal distribution?
2. What are some real-world examples of normally distributed data?
3. How does the 68-95-99.7 rule differ for skewed distributions?
4. What is the significance of being more than 3 standard deviations from the mean?
5. How does the central limit theorem relate to the 68-95-99.7 rule?

Tip:

When analyzing data, always check if it follows a normal distribution before applying the 68-95-99.7 rule, as it won’t apply to non-normal distributions.