Set mean and standard deviation

The normal distribution is centered at its mean and characterized by its standard deviation, which quantifies dispersion. Many types of measurement errors and sample means approximately follow this shape.

I begin by examining the mean and standard deviation, then discuss probability intervals. Merely stating “normal” without specifying parameters yields incomplete information.

In probability theory and statistics, the normal distribution—also known as the Gaussian distribution—is an exceptionally important probability distribution. Its significance stems in part from the Central Limit Theorem. This article will guide you through a deep understanding of the normal distribution’s properties, mathematical formulation, and real-world applications.

What Is the Normal Distribution?

The normal distribution is a continuous probability distribution whose probability density function (PDF) takes the familiar bell-shaped curve. Its formula is:

When learning about the normal distribution, start by inspecting the mean, standard deviation, symmetry, and standardization. Many probabilistic calculations require first converting to the standard normal distribution.

$$f(x; \mu, \sigma) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{(x - \mu)^2}{2\sigma^2}}$$

• Here, $\mu$ denotes the mean (i.e., expected value).
• $\sigma$ denotes the standard deviation, measuring the spread or variability of the data.

Properties of the Normal Distribution

1. Symmetry: The normal distribution is symmetric about its mean $\mu$.

The 68–95–99.7 Rule (Empirical Rule):

• Approximately 68% of the data falls within one standard deviation of the mean ($\mu \pm \sigma$).
• Approximately 95% falls within two standard deviations ($\mu \pm 2\sigma$).
• Approximately 99.7% falls within three standard deviations ($\mu \pm 3\sigma$).

Normality Under Linear Combinations: If $X_1, X_2, \dots, X_n$ are independent and identically distributed (i.i.d.) normal random variables, then any linear combination of them is also normally distributed.

A Practical Example of the Normal Distribution

Suppose we study the heights of residents in a certain city, and know that height follows a normal distribution with mean $\mu = 175\ \text{cm}$ and standard deviation $\sigma = 10\ \text{cm}$. We can compute various probabilities related to height ranges.

Computing the Probability That Height Lies Between 165 cm and 185 cm

First, compute the standardized values (Z-scores):

$$Z_1 = \frac{165 - 175}{10} = -1$$ $$Z_2 = \frac{185 - 175}{10} = 1$$

We can look up these Z-values in a standard normal table—or compute the probability directly using Python:

import scipy.stats as stats

# Set mean and standard deviation
mu = 175
sigma = 10

# Compute probability
prob = stats.norm.cdf(185, mu, sigma) - stats.norm.cdf(165, mu, sigma)
print(f"Probability that height is between 165 cm and 185 cm: {prob:.4f}")

Output:

Probability that height is between 165 cm and 185 cm: 0.6827

This means approximately 68.27% of people in this city have heights between 165 cm and 185 cm—confirming the 68–95–99.7 rule.

Applications of the Normal Distribution

The normal distribution finds widespread use across numerous domains, including:

• Natural and Social Sciences: Many natural phenomena and social metrics—such as human height, weight, and IQ scores—approximate a normal distribution.
• Quality Control: In manufacturing, product characteristics (e.g., length, weight) are often assumed to be normally distributed to support statistical process control.
• Statistical Inference: The normal distribution underpins many classical statistical methods, including t-tests and ANOVA.

After reading this article, consolidate Common Probability Distributions: The Normal Distribution into a concise recap table: first clarify the core narrative, then verify understanding with a small task.

After finishing Common Probability Distributions: The Normal Distribution, try walking through a small example end-to-end, then assess which steps you can now perform independently.

Summary

This article introduced the fundamental concepts, key properties, and practical applications of the normal distribution—and illustrated them with a concrete example. As a cornerstone of probability theory, mastering the normal distribution is essential for conducting more advanced statistical analyses.

After completing Common Probability Distributions: The Normal Distribution, reflect on three questions:

• What problem does it solve?
• At which step is error most likely?
• Can I run through a small example entirely on my own?

In our next article, we’ll explore the Poisson distribution, a discrete probability distribution used to model the number of events occurring within a fixed time interval. Stay tuned!