你需要透彻理解统计里的假设检验。The p-value of a test is the probability of observing a test statistic at least as extreme as the one computed given that the null hypothesis is true. p-value越小，说明如果原假设为真，出现这种情况的可能性很小，所以要拒绝原假设。p-value越大，说明拒绝原假设的可能性越小，但并不等于说原假设为真。统计检验不是为了证明原假设为真。

补充：统计学中的假设检验的基本原理是：小概率事件在一次实验中不会发生，如果发生了，那么我们就有理由怀疑原假设的真实性。P值越小，说明样本统计量的取值等于观测值和更极端值的概率越小，根据小概率事件原理，我们就应该拒绝原假设。

正解

谢谢你们，我在理解下，以前没搞清楚的东西.
加上你们的材料，再补充下：
In statistical hypothesis testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.[1] One often "rejects the null hypothesis" when the p-value is less than the significance level α (Greek alpha), which is often 0.05 or 0.01.

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