The term “normal Gaussian” typically refers to the normal distribution, which is also known as the Gaussian distribution. This probability function describes how the values of a variable are distributed, characterized by its symmetric bell-shaped curve. In a normal distribution, most observations cluster around the central peak, with probabilities tapering off equally in both directions from the mean ¹.

The normal distribution is defined by two parameters: the mean (μ) and the standard deviation (σ). The mean determines the center of the distribution, while the standard deviation controls the width of the curve. A smaller standard deviation results in a narrower curve, whereas a larger standard deviation leads to a wider curve ²⁴.

The normal distribution is significant in statistics and data science due to its properties, such as the empirical rule, which states that approximately 68% of the data falls within one standard deviation of the mean, 95% within two, and 99.7% within three ². This distribution is widely used in various fields, including business and social sciences, due to its applicability in modeling real-world phenomena ³.