“青青子衿，悠悠我心。但为君故，沉吟至今。” 用曹丞相“短歌行”里的这两句诗来 形容我的思想天天被‘统计显著性’所缠绕的情形初看有点牛头不对马嘴，回味一下又似乎颇为贴切。所以特此借用曹丞相“短歌行”里的这两句诗来作为“统计显著性之歌”的引子。

仅以此“统计显著性之歌”献给所有学习、使用、热爱、怀念‘统计显著性’的同学、教师、专家、学者、…。（背景音乐：‘橄榄树’）

不要问我‘统计检验’的理论基础是什么，我的背后有成百上千统计教科书说‘她’魔力无比。为了得出一个结论性的答案，为了论文能发表，我千辛万苦也要得到你-‘统计显著性’。

不要问我什么是费雪的‘显著性检验(significance test)’，也不要问我什么是奈曼-皮尔逊的‘假设检验(test of hypothesis)’，我只需要知道标准统计教科书所教的‘无效假设显著性检验(Null Hypothesis Significance Test)’。

不要问我‘抽样总体’是什么，也不要问我样本数据是如何获取，只要我用上了‘统计检验’一切腐朽都会化作神奇，因为我拥有珍贵无比的‘统计显著性’。

不要问我所研究的问题的科学意义/学科内涵是什么，也不要问我‘统计检验’的分析结果如何从科学意义的角度来解释，只要我‘证明’了的结果是‘统计显著的’，我所想探索的科学发现问题就有了确切的答案。

通过描述性统计数据分析就能逐步揭露科学规律又如何，探索性数据分析才是统计推断分析的本质又如何，‘统计显著性’一枪毙命断生死、判成败才是真‘统计神器’。

弄懂概率分布理论有什么用，统计模型假设条件不能满足又如何，只要找到了‘统计显著性’，一切的无知与担心都会随着学位的获得、文章的分表而随风而去、烟消云散。

管它什么统计抽样分布理论，不要相信什么‘一组抽样数据’无法得出结论性答案的说法，只要你拥有了‘统计显著性’，谁还会去关注什么‘统计抽样分布理论’- 有‘结论性答案’为王 – 管他是用什么方法获取的呢。

不要相信自己的专业训练获得的科学常识，不要运用自己的专业判断能力，因为‘统计显著性’才是判断科学发现成果的‘黄金标准’。

蠢人才去从学科理论、科学实验里费时费力地探寻科学发现，傻瓜才努力想把统计分析与学科理论联系融会贯通，只须好好学会如何从无所不能的统计软件获取‘统计显著性’，科学发现就手到擒来啦！

还有，还有，多多地使用各种你懂也好不懂也好的‘假设检验’来增强你的统计分析的可信度；统计模型越复杂越好，这样得出的结果谁也无法来验证它正确与否，这才显得出你的统计分析功底深厚、水平高超。

还有，还有，不论汝是藏在频率学派还是贝叶斯学派的统计教科书里，也不论是用p-值，还是通过置信区间，还是用贝叶斯统计的后验概率，上天入地，走遍天涯海角我也要找寻汝，找到汝。

统计显著性，统计显著性，亲爱的统计显著性，伴随我进入甜蜜的梦乡！

统计显著性，统计显著性，亲爱的统计显著性，离了汝我可怎么活下去！

在写下了这首酸酸辣辣的“统计显著性之歌”之后，总想着能不能也对应地有一个英文的版本。但实在是觉得自己英文水平不能胜任，以下虽然勉强给出了一个英文翻译结果，却是完全没有了中文版本的韵味。把它拿出来献丑，如果有博友/老师能对本博文的（不论中英文）内容及文字提出纠错/改进的建议，鄙人将不胜感激。

My dearest lady ‘Statistical Significance’: ‘The Sound of Statistical Significance’ dedicated to those researchers and scholars who have learned, or been teaching, and/or been applying ‘statistical significance’, and loved and missed her so much.

Please do not ask me the question “what is the theoretic foundation that underpins ‘statistical significance’?”. What I can tell you is ‘the charm of statistical significance is irresistible’ and ‘her power is beyond measure’ as hundreds of statistics textbooks said so.  In order to be able to claim my research findings being conclusive, hence publishable, it is worth searching for ‘statistical significance’ with all my heart and strength.

I don’t care what Fisher has said in his ‘test of significance’, neither would I care what Neyman-Pearson’s ‘hypothesis test’ is all about.  The only thing I need to care about is the ‘Null Hypothesis Significance Test (NHST)’ as taught in almost every standard statistics textbook.

Please do not bother me with ‘sampling population’ or ‘how my sample data were selected from the population’.  It is my faith that the magic of application of NHST would sift out the uncertainty from my sample data because of the ‘statistical significance’.  That is why ‘statistical significance’ is a treasure in my analysis toolbox.

As long as my analysis results are statistically significant, my scientific research findings would be deemed to be confirmed – conclusive answers found!  What is the point then for me to bother about the disciplinary context or scientific interpretation of my statistical analysis results?

Don’t tell me that ‘scientific findings may be established through repeated experiments’; the statement: ‘the very nature of statistical inference is exploratory’ is not something that I like to hear.  My heart goes with the ‘statistical analysis miracle’ achieved by ‘statistical significance’ – any set of sample data could tell me the definite answer of ‘yes’ or ‘no’; ‘true’ or ‘false’.

Who cares about the probability distribution theory; so what if those model assumption conditions cannot be met?  As long as the ‘statistical significance’ is achieved as the best stepping-stone towards publications and degrees, any ignorance or worries I had about statistics would disappear like clouds flying away from my sight.    

Forget about the ‘sampling distribution’ theory; the view to believe that ‘no scientific findings can be established based on an isolated study’ should not be taken seriously.  ‘Statistical significance’ is Queen in statistical analysis because she grants us the conclusive results and nobody would care how I have found her.

Trust not my scientific common knowledge and apply not my professional judgment because only ‘statistical significance’ is the golden rule to determine scientific research findings.

It is a dumb thing to do to explore scientific findings based on laborious and time-consuming repetitive experiments upon disciplinary theory.  It is wasting of time trying efforts to make connections between the subject context and statistical models. The best way to fast track/achieve our research findings is to search for the ‘statistical significance’ which is a standard/routine analysis output of those wonderful statistical software.

Furthermore, employ as many as possible various ‘hypothesis tests’ to increase the ‘validity’ of my data analysis results; make my statistical models as complex as possible so that analysis outcomes would not testable.  In doing so, I would be able to show how professional and smart I am in performing statistical analysis.

Furthermore, no matter it is with the p-values or confidence intervals in a frequentist approach, or it is with the Bayes factors or posterior probability credible intervals in a Bayesian approach, with all my heart and strength, I shall search for my dearest ‘statistical significance’ until I get hold of her. 

Statistical Significance, Statistical Significance, My dear Statistical Significance!  I shall hold you until I fall asleep into my sweet dreams.

Statistical Significance, Statistical Significance, My dear Statistical Significance!  How could I survive my academic/professional life without you?!