Importance of being uncertain
统计并不会告诉我们我们是不是对的，而是告诉我们我们错误的可能性有多大。
是不是很熟悉? p value 啊。
每当我们做一个重复实验的时候，我们不太可能重复出来完全一样的结果。由于生物的变化以及测量精度的限制，重复的实验将会测量目标值的范围。但是如果每次的测量值都不同，我们如何能说明，这个实验符合我们的假设，能够证明我们的假设理论没有问题？
“科学的最大的悲剧就是，最美丽的假设理论都是被最丑陋的数据所屠戮。”而且这“丑陋”居然还没有办法测量。
科学版的“美女与野兽”。而且当别人问野兽有多丑时，我们只能回答，“有点丑”或者“非常丑”。
然而统计却能帮助我们回答这个问题。它能帮助我们对实验中观察到的某一现象（数据）进行定量的可能性评估。同时还告诉我们数据并不是准确的测量，还是伴有一定错误的估计。
It also tells us how error in input values propagates through calculations. The practical application of this theoretical framework is to associate uncertainty to the outcome of experiments and to assign confidence levels to statements that generalize beyond observations. Although many fundamental concepts in statistics can be under- stood intuitively, as natural pattern-seekers we must recognize the limits of our intuition when thinking about chance and probability. The Monty Hall problem is a classic example of how the wrong answer can appear far too quickly and too credibly before our eyes. A contestant is given a choice of three doors, only one leading to a prize. After selecting a door (e.g., door 1), the host opens one of the other two doors that does not lead to a prize (e.g., door 2) and gives the contestant the option to switch their pick of doors (e.g., door 3). The vexing question is whether it is in the contestant’s best interest to switch. The answer is yes, but you would be in good company if you thought otherwise. When a solution was published in Parade magazine, thousands of readers (many with PhDs) wrote in that the answer was wrong2. Comments varied from “You made a mistake, but look at the positive side. If all those PhDs were wrong, the country would be in some very serious trouble” to “I must admit I doubted you until my fifth grade math class proved you right”2. The Points of Significance column will help you move beyond an intuitive understanding of fundamental statistics relevant to your work. Its aim will be to address the observation that “approximate- ly half the articles published in medical journals that use statistical methods use them incorrectly”3. Our presentation will be practical and cogent, with focus on foundational concepts, practical tips and common misconceptions4. A spreadsheet will often accompany each column to demonstrate the calculations (Supplementary Table 1). We will not exhaust you with mathematics. Statistics can be broadly divided into two categories: descriptive and inferential. The first summarizes the main features of a data set with measures such as the mean and standard deviation (s.d.). The second generalizes from observed data to the world at large. Underpinning both are the concepts of sampling and estimation, which address the process of collecting data and quantifying the uncertainty in these generalizations