如何判定统计分析中的双尾检验与单尾检验

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在做假设检验的时候，我们通常会不假思索的使用显著性水平来解释回归系数。
问题：
我们如何判定某个检验需要用单尾，某个检验需要用双尾？
（问题比较小白，有些困扰）

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关键词：统计分析 计分析 回归系数 假设检验 如何 统计

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也在搜这个问题，刚刚解决：
比如原假设H0：u=15，备择假设H1：u不等于15，这里要否定原假设有两种情况，u小于且大于15，因此需要双尾检验。
如果是H0：u小于15，那么否定原假设只需要证明u大于等于15，就是单尾检验。

Hello KAWA-KAWA,

That depends on your purpose and relevant consequence.

When is a one-tailed test appropriate?
Because the one-tailed test provides more power to detect an effect, you may be tempted to use a one-tailed test whenever you have a hypothesis about the direction of an effect. Before doing so, consider the consequences of missing an effect in the other direction.  Imagine you have developed a new drug that you believe is an improvement over an existing drug.  You wish to maximize your ability to detect the improvement, so you opt for a one-tailed test. In doing so, you fail to test for the possibility that the new drug is less effective than the existing drug.  The consequences in this example are extreme, but they illustrate a danger of inappropriate use of a one-tailed test.

So when is a one-tailed test appropriate? If you consider the consequences of missing an effect in the untested direction and conclude that they are negligible and in no way irresponsible or unethical, then you can proceed with a one-tailed test. For example, imagine again that you have developed a new drug. It is cheaper than the existing drug and, you believe, no less effective.  In testing this drug, you are only interested in testing if it less effective than the existing drug.  You do not care if it is significantly more effective.  You only wish to show that it is not less effective. In this scenario, a one-tailed test would be appropriate.

When is a one-tailed test NOT appropriate?
Choosing a one-tailed test for the sole purpose of attaining significance is not appropriate.  Choosing a one-tailed test after running a two-tailed test that failed to reject the null hypothesis is not appropriate, no matter how "close" to significant the two-tailed test was.  Using statistical tests inappropriately can lead to invalid results that are not replicable and highly questionable--a steep price to pay for a significance star in your results table!   

I hope this helps.

xdx0330 发表于 2015-2-9 22:09
也在搜这个问题，刚刚解决：
比如原假设H0：u=15，备择假设H1：u不等于15，这里要否定原假设有两种情况，u ...

有点理解了，谢谢

xdx0330 发表于 2015-2-9 22:09
也在搜这个问题，刚刚解决：
比如原假设H0：u=15，备择假设H1：u不等于15，这里要否定原假设有两种情况，u ...

有点理解了，谢谢