正态分布

The normal distribution is a continuous probability distribution with a symmetrical, bell-shaped graph described by the Gaussian distribution formula. It is the most important distribution across disciplines including psychology, business, economics, sciences, and nursing. The curve is symmetric about the mean, and the area under the curve equals one. Most real-world phenomena like IQ scores and real-estate prices follow this distribution.

The normal distribution depends only on two parameters: mean (μ) and standard deviation (σ). Changes in standard deviation alter the curve's shape, making it fatter or skinnier. Changes in mean shift the graph left or right. This means there are infinite normal probability distributions, each uniquely defined by its mean and standard deviation values.

The standard normal distribution is a normal distribution of standardized values called z scores, where the mean is zero and standard deviation is one. Converting a normal distribution into standard normal distribution simplifies the Gaussian distribution formula and eases probability calculations. This standardization also allows comparison of datasets with different means and standard deviations.

A z score is measured in units of standard deviation from the mean. For example, if the mean is five and standard deviation is two, the value 11 is three standard deviations above the mean, giving a z score of 3. The z score formula standardizes any value from a normal distribution, enabling comparison across different datasets and probability calculations.

A normal distribution is symmetric about a vertical line drawn through the mean. This symmetry means the mean equals the median in theory, as the curve mirrors itself on both sides. The bell-shaped graph reflects this perfect balance, with equal proportions of data distributed on either side of the center value.

Although the normal distribution is extremely important and widely observed, it cannot be applied to everything in the real world. While many phenomena like birth weights, test scores, and prices follow this pattern, not all datasets conform to a bell-shaped curve. Understanding when the normal distribution applies is essential for accurate statistical analysis and interpretation.

Standardizing data into z scores simplifies calculations and enables meaningful comparisons between datasets with different means and standard deviations. The z score formula converts any normal distribution value into the standard normal distribution framework, where the mean is zero and standard deviation is one. This uniform scale makes probability calculations and data interpretation more straightforward across diverse applications.