统计显著(p value)是什么

http://www.statsoft.com/textbook/esc.html

p值越低，我们对某个变量间关系的样本观测值出错的可能性就越小

看spss的简单解释：

提出原假设和备择假设，H0和H1
2、确定适当的检验H0的统计量
3、规定显著性水平α
4、计算检验统计量的值
5、根据这个实现值计算p值
6、作出统计决策
（如果p值小于或等于a，就拒绝零假设，这时犯错误的概
率最多为a；如果p值大于a，就不拒绝零假设，因为证
据不足）

The higher the p-value, the less we can believe that the observed relation between variables in the sample is a reliable indicator of the relation between the respective variables in the population. Specifically, the p-value represents the probability of error that is involved in accepting our observed result as valid, that is, as "representative of the population." For example, a p-value of .05 (i.e.,1/20) indicates that there is a 5% probability that the relation between the variables found in our sample is a "fluke." In other words, assuming that in the population there was no relation between those variables whatsoever, and we were repeating experiments like ours one after another, we could expect that approximately in every 20 replications of the experiment there would be one in which the relation between the variables in question would be equal or stronger than in ours

题外话：观察100个随机变量（45对correlation关系），也有机会发现2个“相关关系“的显著度p<0.05

小关系只能在大量样本中才可能被证实显著

Consider the following additional illustration. If a coin is slightly asymmetrical, and when tossed is somewhat more likely to produce heads than tails (e.g., 60% vs. 40%), then ten tosses would not be sufficient to convince anyone that the coin is asymmetrical, even if the outcome obtained (six heads and four tails) was perfectly representative of the bias of the coin. However, is it so that 10 tosses is not enough to prove anything? No, if the effect in question were large enough, then ten tosses could be quite enough. For instance, imagine now that the coin is so asymmetrical that no matter how you toss it, the outcome will be heads. If you tossed such a coin ten times and each toss produced heads, most people would consider it sufficient evidence that something is "wrong" with the coin. In other words, it would be considered convincing evidence that in the theoretical population of an infinite number of tosses of this coin there would be more heads than tails. Thus, if a relation is large, then it can be found to be significant even in a small sample.