假设检验 检验的p值

When you run the hypothesis test, the test will give you a value for p. Compare that value to your chosen alpha level. For example, let’s say you chose an alpha level of 5% (0.05). If the results from the test give you:

• A small p (≤ 0.05), reject the null hypothesis. This is strong evidence that the null hypothesis is invalid.
• A large p (> 0.05) means the alternate hypothesis is weak, so you do not reject the null

https://www.statsdirect.com/help/basics/p_values.htm

The term significance level (alpha) is used to refer to a pre-chosen probability and the term "P value" is used to indicate a probability that you calculate after a given study.

If your P value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis. 

The significance level (alpha) is the probability of type I error. 

Notes about Type I error:

• is the incorrect rejection of the null hypothesis
• maximum probability is set in advance as alpha
• is not affected by sample size as it is set in advance
• increases with the number of tests or end points (i.e. do 20 rejections of H₀ and 1 is likely to be wrongly significant for alpha = 0.05)

Notes about Type II error:

• is the incorrect acceptance of the null hypothesis
• probability is beta
• beta depends upon sample size and alpha
• can‘t be estimated except as a function of the true population effect
• beta gets smaller as the sample size gets larger
• beta gets smaller as the number of tests or end points increases