In statistical terms, what is a “Normal Distribution”?

A Normal Distribution is defined as a symmetric distribution that typically features a bell-shaped curve. In this distribution, the majority of the data points are concentrated around a central value, which is also the mean, median, and mode of the dataset. The symmetry implies that the values are equally likely to fall above or below the mean, creating a situation where approximately 68% of the data lies within one standard deviation, about 95% within two standard deviations, and 99.7% within three standard deviations from the mean. This clustering of data points around the central peak reflects the essence of normal distribution, making it a fundamental concept in statistics, especially in the context of Six Sigma methodologies.

The other options describe different types of distributions that do not fit the characteristics of a Normal Distribution. A skewed distribution indicates the presence of outliers, which would disrupt the symmetry. A uniform distribution would mean all values have the same frequency, which is contrary to the bell-shaped curve of a normal distribution. Lastly, a random distribution lacks any identifiable pattern or central tendency, which also diverges from the defined structure of a normal distribution. Understanding these distinctions is crucial for applying statistical principles effectively in quality management and process improvement.