Interpretation of probability

Probability, also known as "contingency", reflects the likelihood that a random event will occur. A random event is an event that may or may not occur under the same conditions. For example, from a batch of genuine and defective goods, randomly select a piece, "the draw is genuine" is a random event. Let a random phenomenon n times test and observation, which A event appeared m times, that is, the frequency of its appearance for m / n. After a large number of repeated tests, often m / n closer and closer to a certain constant (the proof of this assertion see Bernoulli's Law of Large Numbers). This constant is the probability of occurrence of event A, often expressed as P (A).

Probability is a numerical measure of the likelihood of a chance event occurring. Suppose a chance event (represented by A) occurs a number of times (represented by Y) after a number of repeated trials (represented by X). Using X as the denominator and Y as the numerator, a numerical value (represented by P) is formed. Over many trials, P is relatively stable at a certain value, and P is called the probability of A occurring. If the probability of a chance event is determined by long-term observation or a large number of repeated trials, this probability is statistical or empirical.

The study of the inner laws that govern chance events is called probability theory. It belongs to a branch of mathematics. Probability theory reveals the manifestations of the internal laws that govern chance phenomena. Probability, therefore, plays an important role in people's understanding of natural and social phenomena.

Probability and Poisson distribution

The Poisson distribution is suitable for describing the number of times a random event occurs per unit of time. The Poisson distribution combined with historical data allows you to calculate the number of goals possible in a soccer match. Use the simple Poisson distribution formula to calculate the probability of scoring a goal or result in any given soccer match.

Probability event

In probability theory, the probability is very close to 0 (i.e., in a large number of repeated experiments in the frequency of occurrence is very low) of the event is called a small probability event. Generally used more than 0.01 ~ 0.05 two values that is, the probability of the event occurs in 0.01 or less or 0.05 or less of the event is called a small probability event these two values are called small probability criterion.

Small probability event is an event with a small probability of occurrence, then it is almost impossible to occur in a test, but in many repeated tests is bound to happen.

----------------------The following is the original Chinese version----------------------------

概率的解释

概率，亦称“或然率”，它是反映随机事件出现的可能性大小。随机事件是指在相同条件下，可能出现也可能不出现的事件。例如，从一批有正品和次品的商品中，随意抽取一件，“抽得的是正品”就是一个随机事件。设对某一随机现象进行了n次试验与观察，其中A事件出现了m次，即其出现的频率为m/n。经过大量反复试验，常有m/n越来越接近于某个确定的常数（此论断证明详见伯努利大数定律）。该常数即为事件A出现的概率，常用P (A) 表示。

概率是度量偶然事件发生可能性的数值。假如经过多次重复试验(用X代表)，偶然事件(用A代表)出现了若干次(用Y代表)。以X作分母，Y作分子，形成了数值(用P代表)。在多次试验中，P相对稳定在某一数值上，P就称为A出现的概率。如偶然事件的概率是通过长期观察或大量重复试验来确定，则这种概率为统计概率或经验概率。

研究支配偶然事件的内在规律的学科叫概率论。属于数学上的一个分支。概率论揭示了偶然现象所包含的内部规律的表现形式。所以，概率，对人们认识自然现象和社会现象有重要的作用。

概率与泊松分布

泊松分布适合于描述单位时间内随机事件发生的次数。泊松分布结合历史数据，可以计算足球比赛中可能的进球数。 使用简单的泊松分布公式计算任何给定的足球比赛得分或结果的概率。

小概率事件

在概率论中把概率很接近于0（即在大量重复试验中出现的频率非常低）的事件称为小概率事件。一般多采用0.01~0.05两个值即事件发生的概率在0.01以下或0.05以下的事件称为小概率事件这两个值称为小概率标准。

小概率事件是一个事件的发生概率很小，那么它在一次试验中是几乎不可能发生的，但在多次重复试验中是必然发生的。