1、Alpha Level (Significance Level，顯著水平): What is it?
顯著性水平α是指當零假設是正確的，但做出了錯誤決策的概率(即一類錯誤的概率)。Alpha水平(有時稱為“顯著性水平”)用於假設測試。通常，這些測試的alpha值為0.05(5%)，但是其他常用的值是0.01和0.10。
The significance level α is the probability of making the wrong decision when the null hypothesis is true. Alpha levels (sometimes just called “significance levels”) are used in hypothesis tests. Usually, these tests are run with an alpha level of .05 (5%), but other levels commonly used are .01 and .10.
在了解之前首先看一下什么是一類錯誤和二類錯誤
2. Alpha Levels / Significance Levels: Type I and Type II errors
在假設檢驗中，有兩種錯誤是可能的，第一類錯誤和第二類錯誤。
一類錯誤:當原假設為真時，卻支持了備擇假設。
二類錯誤:當備擇假設為真時，卻不支持備擇假設。
In hypothesis tests, two errors are possible, Type I and Type II errors. Type I error: Supporting the alternate hypothesis when the null hypothesis is true. Type II error: Not supporting the alternate hypothesis when the alternate hypothesis is true
在法庭的例子中，零假設是一個人是無辜的備擇假設是他有罪。如果你判定一個無辜的人有罪(第一類錯誤)，即你支持備擇假設(他有罪)。第二類錯誤是讓罪犯逍遙法外。
In an example of a courtroom, let’s say that the null hypothesis is that a man is innocent and the alternate hypothesis is that he is guilty. if you convict an innocent man (Type I error), you support the alternate hypothesis (that he is guilty). A type II error would be letting a guilty man go free.
a級是第一類錯誤的概率，也就是拒絕零假設的概率。一個相關的術語，beta,是相反的;表示當替代假設為真時，拒絕它的概率。
An alpha level is the probability of a type I error, or you reject the null hypothesis when it is true. A related term, beta, is the opposite; the probability of rejecting the alternate hypothesis when it is true.
Alpha水平可以由你控制，並與自信水平相關。例如，如果你想要有95%的把握你的分析是正確的，假設你有一個單側檢驗，那么alpha水平應該是1 -0.95 = 5%。對於雙尾檢驗，將alpha值除以2,在這個例子中，兩個尾部是0.05/2 = 2.5%
Alpha levels can be controlled by you and are related to confidence levels. To get α subtract your confidence level from 1. For example, if you want to be 95 percent confident that your analysis is correct, the alpha level would be 1 – .95 = 5 percent, assuming you had a one tailed test. For two-tailed tests, divide the alpha level by 2. In this example, the two tailed alpha would be .05/2 = 2.5 percent. See: One-tailed test or two? for the difference between a one-tailed test and a two-tailed test.
3. Why is an alpha level of .05 commonly used?
由於alpha級別是犯第I類錯誤的概率，我們將這個區域盡可能地縮小似乎是有意義的。例如，如果我們將alpha水平設置為10%，那么我們很有可能錯誤地拒絕零假設，而alpha水平為1%則會使面積變小。那么為什么不使用一個很小的區域而不是標准的5%呢?
Seeing as the alpha level is the probability of making a Type I error, it seems to make sense that we make this area as tiny as possible. For example, if we set the alpha level at 10% then there is large chance that we might incorrectly reject the null hypothesis, while an alpha level of 1% would make the area tiny. So why not use a tiny area instead of the standard 5%?
alpha level越小，拒絕面積越小即你拒絕零假設的概率越小。如果該區域越小，你拒絕零假設的幾率越小。但當事實上你需要拒絕零假設，這時候你就犯了II類錯誤。也就是說你避免I類錯誤的幾率越小，那么你犯二類錯誤的幾率越大。科學家發現alpha level為5%是兩類錯誤的good平衡。
The smaller the alpha level, the smaller the area where you would reject the null hypothesis. So if you have a tiny area, there’s more of a chance that you will NOT reject the null, when in fact you should. This is a Type II error. In other words, the more you try and avoid a Type I error, the more likely a Type II error could creep in. Scientists have found that an alpha level of 5% is a good balance between these two issues.
這張圖顯示了最右邊的拒絕區域。即alpha level（即拒絕零假設的概率，或者說是犯一類錯誤的概率）。該區域越小，即你拒絕零假設的概率越小，越傾向於接受零假設，這樣增加了II類錯誤的概率。