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p-value and Bayes are the two hottest words in Statistics. Actually I still can not get why the debate between frequentist statistics and Bayesian statistics can last so long. What is the essence arguments behind it? (Any one can help me with this?) In my point of view, they are just two ways for solving practical problems. Frequentist people are using the random version of proof-by-contradiction argument (i.e. small p-value indicates less likeliness for the null hypothesis to be true), while Bayesian people are using learning argument to update their believes through data. Besides, mathematician are using partial differential equations (PDE) to model the real underlying process for the analysis. These are just different methodologies for dealing with practical problems. What’s the point for the long-last debate between frequentist statistics and Bayesian statistics then?

Although my current research area is mostly in frequentist statistics domain, I am becoming more and more Bayesian lover, since it’s so natural. When I was teaching introductory statistics courses for undergraduate students at Michigan State University, I divided the whole course into three parts: Exploratory Data Analysis (EDA) by using R software, Bayesian Reasoning and Frequentist Statistics. I found that at the end of the semester, the most impressive example in my students mind was the one from the second section (Bayesian Reasoning). That is the Monty Hall problem, which was mentioned in the article that just came out in the NYT. (Note that about the argument from Professor Andrew Gelman, please also check out the response from Professor Gelman.) “Mr. Hall, longtime host of the game show “Let’s Make a Deal,” hides a car behind one of three doors and a goat behind each of the other two. The contestant picks Door No. 1, but before opening it, Mr. Hall opens Door No. 2 to reveal a goat. Should the contestant stick with No. 1 or switch to No. 3, or does it matter?” And the Bayesian approach to this problem “would start with one-third odds that any given door hides the car, then update that knowledge with the new data: Door No. 2 had a goat. The odds that the contestant guessed right — that the car is behind No. 1 — remain one in three. Thus, the odds that she guessed wrong are two in three. And if she guessed wrong, the car must be behind Door No. 3. So she should indeed switch.” What a natural argument! Bayesian babies and Google untrained search for youtube cats (the methods of deep learning) are all excellent examples proving that Bayesian Statistics IS a remarkable way for solving problems.

What about the p-values? This random version of proof-by-contradiction argument is also a great way for solving problems from the fact that it have been helping solve so many problems from various scientific areas, especially in bio-world. Check out today’s post from Simply Statistics: “You think P-values are bad? I say show me the data,” and also the early one: On the scalability of statistical procedures: why the p-value bashers just don’t get it.

The classical p-value does exactly what it says. But it is a statement about what would happen if there were no true effect. That can’t tell you about your long-term probability of making a fool of yourself, simply because sometimes there really is an effect. You make a fool of yourself if you declare that you have discovered something, when all you are observing is random chance. From this point of view, what matters is the probability that, when you find that a result is “statistically significant”, there is actually a real effect. If you find a “significant” result when there is nothing but chance at play, your result is a false positive, and the chance of getting a false positive is often alarmingly high. This probability will be called “false discovery rate” (or error rate), which is different with the concept in the multiple comparison. One possible misinterpretation of p-value is regarding p-value as the false discovery rate, which may be much higher than p-value. Think about the Bayes formula and the tree diagram you learned in introductory course to statistics to figure out the relationship between p-value and the “false discovery rate”.

I collected the following series on applying for faculty positions in 2011, when I was in my second year PhD. Now it’s my turn to apply for jobs. I will share the following useful materials with all you who want to apply for jobs this year.

- Some R Resources for GLMs
- 失联搜救中的统计数据分析
- The gap between data mining and predictive models
- Data Mining, machine learning and statistics.
- useR! 2014 is underway with 16 tutorials
- What is Scalable Machine Learning?
- rlist：基于list在R中处理非关系型数据
- The perfect candidate
- The Leek group guide to giving talks
- 38 Seminal Articles Every Data Scientist Should Read
- Deep Learning – important resources for learning and understanding
- Twenty rules for good graphics + Ten Simple Rules for Better Figures
- Git Cookbook
- Making Your Code Citable
- biblatex for statisticians
- Do your “data janitor work” like a boss with dplyr

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