The term “normal Gaussian” typically refers to the normal distribution, which is a probability distribution that is symmetric and bell-shaped. It is characterized by its mean (average) and standard deviation (a measure of spread). The normal distribution is significant in statistics and data science because many real-world phenomena, such as heights or measurement errors, tend to follow this distribution pattern.

The normal distribution is defined mathematically, and its properties include that the mean, median, and mode are all equal and located at the center of the distribution. The area under the curve of a normal distribution equals 1, and it follows the empirical rule, which states that 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 ¹².

In data science, the assumption of normality simplifies analysis and is computationally efficient, making it a common choice for modeling various variables ³.