Application of normal distribution in talent recruitment

Normal distribution

normal distribution

A probability distribution. The normal distribution is a distribution of continuous random variables with two parameters μ and σ2. The first parameter μ is the mean of the random variable that follows the normal distribution, and the second parameter σ2 is the random variable. The variance of , so the normal distribution is recorded as N(μ, σ2). The probability rule of a random variable that follows a normal distribution is that the probability of taking a value near μ is high, and the probability of taking a value farther away from μ is smaller; the smaller σ, the more concentrated the distribution is near μ; the larger σ, the smaller the distribution. dispersion. The characteristics of the density function of the normal distribution are: symmetric about μ, reaching a maximum value at μ, taking a value of 0 at positive (negative) infinity, and having an inflection point at μ±σ. Its shape is high in the middle and low on both sides, and the image is a bell-shaped curve above the x-axis. When μ = 0, σ2 = 1, it is called the standard normal distribution, recorded as N (0, 1). When a μ-dimensional random vector has similar probability rules, it is said that the random vector follows a multi-dimensional normal distribution. The multivariate normal distribution has very good properties. For example, the marginal distribution of the multivariate normal distribution is still a normal distribution, and the random vector obtained by any linear transformation is still a multidimensional normal distribution. In particular, its linear combination is a one-dimensional normal distribution. distributed.

The normal distribution was first obtained by A. Demoivre in finding the asymptotic formula of the binomial distribution. C.F. Gauss derived it from another angle when studying measurement error. P.S. Laplace and Gauss studied its properties.

The probability distribution of many random variables in production and scientific experiments can be approximately described by the normal distribution. For example, when the production conditions remain unchanged, the strength, compressive strength, caliber, length and other indicators of the product; the length, weight and other indicators of the same organism; the weight of the same seed; the error in measuring the same object; the impact point along the Deviation in a certain direction; annual precipitation in a certain area; and velocity components of ideal gas molecules, etc. Generally speaking, if a quantity is the result of many small independent random factors, then the quantity can be considered to have a normal distribution (see the central limit theorem). Theoretically, the normal distribution has many good properties, and many probability distributions can be approximated by it; there are also some commonly used probability distributions that are directly derived from it, such as lognormal distribution, t distribution, F distribution, etc. .

The normal distribution is the most widely used continuous probability distribution, which is characterized by a "bell" shaped curve.