What is Empirical Rule?
Empirical Rule, also known as the 68-95-99.7 Rule, is a statistical guideline that describes the distribution of data in a normal distribution. It states that in a bell-shaped curve, approximately 68% of the data falls within one standard deviation from the mean, about 95% within two standard deviations, and nearly 99.7% within three standard deviations. This rule provides a quick way to understand the spread of data and is applicable in various fields for analyzing and interpreting distributions.
Normal Distribution
A normal distribution, also known as a Gaussian distribution or bell curve, is a statistical concept that represents the probability distribution of a continuous random variable. In a normal distribution:
- Data is symmetrically distributed around the mean, with the highest frequency of observations at the mean.
- Curve is bell-shaped, with tails extending infinitely in both directions.
- Mean, Median and Mode are all equal and located at the center of the distribution.
- Standard deviation determines the spread or dispersion of data. About 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.
Empirical Rule
Empirical Rule, also known as the 68-95-99.7 rule, states that in a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.
In this article we will understand, Empirical Rule, Normal Distribution, Standard Deviation, Applications of Empirical Rule, Empirical Rule Formula, and others in detail.
Table of Content
- What is Empirical Rule?
- Normal Distribution
- Empirical Rule and Standard Deviation
- How Does Empirical Rule Work?
- Formula of Empirical Rule
- Empirical Rule Vs Chebyshev’s Theorem
- Chebyshev’s Theorem
- Applications of Empirical Rule