The **normal distribution** is used extensively in probability theory, statistics, and the natural and social sciences. It is also called the **Gaussian distribution** because Carl Friedrich Gauss (1777-1855) was one of the first to apply it for the analysis of astronomical data (Lovric 2011).

The **normal probability distribution** or the **normal curve** is a bell-shaped (symmetric) curve. Its mean is denoted by \(\mu\) and its standard deviation by \(\sigma\). A continuous random variable \(x\) that has a normal distribution is called a **normal random variable**.

The notation for a normal distribution is \(X \sim N(\mu,\sigma)\). The probability density function (PDF) is written as

\[f(x) = \frac{1}{\sigma \sqrt{2 \pi}}e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma}\right)^2}\]

where \(e \approx 2.71828\) and \(\pi \approx 3.14159\). The probability density function \(f(x)\) gives the vertical distance between the horizontal axis and the normal curve at point \(x\).

The normal distribution is described by two parameters, the mean, \(\mu\), and the standard deviation, \(\sigma\). Each different set of values of \(\mu\) and \(\sigma\) gives a different normal distribution. The value of \(\mu\) determines the center of a normal distribution curve on the horizontal axis, and the value of \(\sigma\) gives the spread of the normal distribution curve.

A normal distribution is characterized, among others, by the following characteristics (Lovric 2011, Mann 2012):

- The total area under a normal distribution curve is 1.0, or 100%.

- A normal distribution curve is symmetric about the mean. Consequently, 50% of the total area under a normal distribution curve lies on the left side of the mean, and 50% lies on the right side of the mean.

- The tails of a normal distribution curve extend indefinitely in both directions without touching or crossing the horizontal axis. Although a normal distribution curve never meets the horizontal axis, beyond the points represented by \(\mu - 3\sigma\) and \(\mu + 3\sigma\) it becomes so close to this axis that the area under the curve beyond these points in both directions can be taken as virtually zero.