統計分析中Type I Error與Type II Error的區別

在統計分析中，經常提到Type I Error和Type II Error。他們的基本概念是什麼？有什麼區別？
下麵的表格顯示 between truth/falseness of the null hypothesis and outcomes of the test
"
-------|-------|-------
| Judgement of Null Hypothesis H0 | Valid | Invalid |
| Reject |Type I Error (False Positive)|Correct (True Positive)|
| Fail to Reject | Correct (True Negative)|Type II Error (False Negative)|
-------|-------|-------
"

Type I error：

false positive,
Testing shows that something is present, but it is not. incorrect detection of something.

Type II error:

false negative,
Testing shows that something is not present, but in fact it is present. Fail to detect something.

In statistical hypothesis testing, a type I error is the incorrect rejection of a true null hypothesis (a "false positive"), while a type II error is the failure to reject a false null hypothesis (a "false negative").