Statistics 简明教程
Statistics - Statistical Significance
统计显著性表示统计实验或检验的结果不是随机发生的，而是可以归因于某种原因。结果的统计显著性可以很强或很弱，这对于严重依赖研究工作的行业（如保险、制药、金融、物理等）非常重要。
Statistical Significance signifies that result of a statistical experiment or testing is not occuring randomly and is attributable to certain cause. Statistical significance of a result could be strong or weak and it is very important for sectors which are heavily dependent on research works like insurance, pharma, finance, physics and so.
统计显著性有助于选择样本数据，以便可以判断检验的结果或结果是否真实，而不是由随机原因引起的。
Statistical Significance helps in choosing the sample data so that one can judge the result or outcome of testing to be realistic and not be caused by a random cause.
统计学家通常通过抽样误差来制定统计显著性程度。通常可以接受 5% 的抽样误差。样本量也很重要，因为样本量应该是具有代表性的样本，而不是非常大的样本，因为大样本容易出错。
Statisticians generally formulates the degree of statistical significance by sampling error. Generally sampling error of 5% is acceptable. Sample size is also important as it should be representative sample instead of very large sample considering the fact that large samples are prone to errors.
Significance Level
事件被认为具有统计显著性的水平被称为显著性水平。统计学家使用称为 p 值的检验统计量来获得统计显著性。如果事件的 p 值低于特定水平，则该事件被认为具有统计显著性。p 值是样本数据的标准差和平均值的函数。p 值是一个事件的概率，它证明统计检验的结果是偶然发生的还是由于某种抽样误差造成的。换句话说，它是统计检验失败的风险。p 值的对立面是置信水平，即 1 - p 值。
A level at which an event is considered to be statistical significant is termed as significance level. Statisticians uses a test statistic called p-value to get the statistical significance. If p-value of an event falls below a particular level then the event is considered as statistical significant. p-value is function of standard deviations and means of data samples. p-value is the probability of an event which certifies that result of statistical testing is occuring by chance or due to some sampling error. In other words it is the risk of failure of a statistical test. Opposite of p-value is confidence level which is 1 - p-value.
如果结果的 p 值为 5%，则意味着结果的置信度为 95%。
If p-value of a result is 5% then that means confidence level of the result is 95%.