p是概率吗？ (Is p for probability?)

Technically, p-value stands for probability value, but since all of statistics is all about dealing with probabilistic decision-making, that’s probably the least useful name we could give it.

从技术上讲， p值代表概率值 ，但是由于所有统计数据都涉及概率决策，因此 ，这可能是我们可以给它提供的最不实用的名称。

Instead, here are some more colorful candidate names for your amusement.

相反，这里有一些更有趣的候选名称供您娱乐。

Painful value: They make you calculate it in class without explaining it to you properly; no wonder your brain is hurting. Honorable submissions in this category also include puzzling value, perplexing value, and punishing value.

痛苦的价值：他们使您无法在课堂上正确地进行计算； 难怪你的大脑受伤了。 此类别中的荣誉提交还包括令人困惑的价值 ， 困惑的价值和惩罚性的价值 。

Pesky value / problematic value: Statisticians are so tired of seeing ignoramuses abuse the p-value that some of them want to see it abolished. They wish they could shake people, yelling, “It’s a tool for personal decision-making, not that other thing you think it is!”

讨厌的价值/有问题的价值：统计学家对看到无用的滥用p值感到厌倦，以致于有些人希望看到 p值被废除 。 他们希望他们能打动人们，大喊：“这是个人决策的工具，而不是您认为的其他东西！”

Persuasive value: As I’ll explain in a moment, trying to use a p-value to persuade someone is a dangerous bet that your victim is more ignorant than you are. If you’re going to appeal to p-values to spice up your message, may I recommend rewriting all your arguments in Latin while you’re at it?

有说服力的价值：正如我稍后会解释的那样，尝试使用p值说服某人是一个危险的赌注，认为您的受害者比您更无知。 如果您打算使用p值来增加信息的趣味性，我是否建议您在使用拉丁语时重写所有参数？

Publishable value: Speaking of ways to abuse the p-value, if you’re one of those “scientists” who feels no remorse torturing (“p-hacking”) your data until it confesses the kind of p-value you think will impress reviewers of an academic journal, you’re part of the problem and not the solution.

可发布的价值：说到滥用p值的方式，如果您是那些不会scientists悔折磨(“ p hacking”)数据直到承认自己认为会令人印象深刻的p值的“ 科学家 ”之一，学术期刊的审稿人，您是问题的一部分，而不是解决方案。

Pay value: If you think academia is the only place where your salary depends on your ability to cook up good-lookin’ p-values, think again!

薪水价值：如果您认为学术界是薪水取决于您制定好看的p值的能力的唯一地方，请再考虑一遍！

Punchline value: Classical statistical inference boils down to asking “Does the evidence we collected make the null hypothesis look ridiculous?” The p-value is the punchline, summarizing the answer to this big testing question in one little number.

关键点价值：经典的统计推断归结为： “我们收集的证据是否使原假设变得荒谬？” p值是重点，将这个大测试问题的答案归纳为一个小数目。

Plausibility value: The higher the p-value, the more plausible your evidence looks in a universe where we’re not totally nuts to stick to our default action. Notice that this is about the plausibility of your evidence in a particular kind of world… NOT the plausibility of that world itself!

合理性值： p值越高，您的证据在宇宙中的可信度就越高，在该宇宙中我们并非完全不愿意采取默认行动 。 请注意，这是关于您的证据在特定世界中的合理性……而不是该世界本身的合理性！

Passivity value: The higher your p-value, the less reason you have to change your mind. Keep doing whatever you passively planned to do. To understand why, read on. (But bear in mind that a lack of evidence is not the same thing as evidence of a lack. A silent smoke alarm doesn’t always mean there’s no fire.)

被动值： p值越高，改变主意的原因就越少。 继续做您打算做的事。 要了解原因，请继续阅读。 (但是请记住， 缺乏证据是不一样的东西缺乏证据 。一个无声的烟雾报警器并不总是意味着没有火灾。)

P是打Kong！ (P is for Punchline!)

Remember how we boiled statistical inference down to one sentence? It was:

还记得我们如何将统计推断简化为一个句子吗？ 它是：

Does the evidence we collected make our null hypothesis look ridiculous?

我们收集的证据是否使我们的零假设看起来很荒谬？

The p-value is the punchline to that question. It summarizes the answer in one little number. The lower the p-value, the more ridiculous the null hypothesis looks!

p值是该问题的重点。 它总结了几个答案。 p值越低，原假设看起来就越荒谬！

So, how do we turn the answer into a yes or a no? We simply set a threshold in advance to indicate what’s ridiculous enough to change our minds. The fancy name for that threshold is the significance level. If the p-value is below it, change your mind. If not, keep doing what you were happy to do by default.

那么，我们如何将答案变成是或否？ 我们只是简单地预先设置一个阈值，以表明什么足以改变我们的想法的荒谬。 该阈值的奇特名称是显着性水平 。 如果p值低于该值，请改变主意。 如果没有，默认情况下继续做自己喜欢做的​​事情。

*是*与*做* (What it *is* versus what it *does*)

A wonderful thing about p-values is that they’re easy and relatively safe to use… if you picked the right test for your null hypothesis and assumptions. (That’s a big if!) But don’t forget that what you’ve just learned is what they do, not what they are.

关于p值的一个奇妙之处在于，它们易于使用且相对安全...如果您为无效假设和假设选择了正确的检验，则可以使用。 (如果这么大的话！)但是请不要忘记，您刚刚学到的是他们在做什么 ，而不是他们在 做什么。

Don’t make the mistake of trying to understand what they are in a pithy one-liner.

不要试图去理解它们在一个简单的单行代码中的错误。

What they are is something weird: probability statements about samples in a specific imaginary universe. They’re most definitely not that straight-forward thing you want them to be; they weren’t designed to be intuitive to interpret or pithy to describe. They’re made for reading off the output of a hypothesis test.

他们都是奇怪的事情：有关特定假想的宇宙样品的概率声明。 它们绝对不是您希望它们成为的简单明了的东西； 他们的目的不是要直观地解释或难以描述。 它们用于读取假设检验的输出。

So, what are they? To see that, you’ll need to understand how we calculate them. I’ve written about that in my other articles, e.g. here, so I’ll stick to a summary here.

那是什么 要看到这一点，您需要了解我们如何计算它们。 我已经在其他文章(例如here)中对此进行了介绍，因此在这里我将坚持摘要。

简介：您如何*获得* p值？ (Summary: How do you *get* a p-value?)

Calculating a p-value is a five-step process.

计算p值是一个五步过程。

1. Choose the default action.

   选择默认操作 。

2. State the null hypothesis.

   陈述原假设 。

3. State the assumptions about how the world described by that null hypothesis works.
   陈述有关该原假设所描述的世界如何工作的假设。
4. Make a model of that world (using equations or simulation) — this is the bulk of the work for statisticians.
   创建该世界的模型(使用方程式或模拟)-这是统计学家的主要工作。
5. Find the probability that this world coughs up evidence at least as bad as we’re seeing in our real-life data.
   找到这个世界咳嗽证据的可能性至少与我们在现实生活数据中看到的一样糟糕。

摘要：如何*使用* p值？ (Summary: How do you *use* a p-value?)

1. Compare it against the significance level.

   将其与显着性水平进行比较。

2. Change your mind if the p-value is below the significance level. Otherwise, just keep doing what you were going to do if you never analyzed any data.

   如果p值低于显着性水平，请改变主意。 否则，只要您从未分析过任何数据，就继续做您打算做的事情。

摘要：简短说明 (Summary: Short explanation)

A p-value asks, “If I’m living in a world where I should be taking my default action, how unsurprising is my evidence?” The higher the p-value, the less ridiculous I’ll feel about persisting with my planned action. If the p-value is low enough, I’ll change my mind and do something else.

一个p值询问： “如果我生活在应该采取 默认行动 的世界中 ，我的证据有多令人惊讶？” P值越高，我坚持执行计划中的动作就越可笑。 如果p值足够低，我会改变主意并做其他事情。

Polemical value / polarizing value: If you want to learn about the p-value controversy and read my take on all the emotions the p-value causes, check out the next article in this series: Why are p-values like needles?

政治价值/极化价值： 如果您想了解 p值的争议 并阅读我对p值引起的所有情绪的看法，请查看本系列的下一篇文章： 为什么p值像针一样？

使用p值的最安全方法 (The safest way to use a p-value)

In order to interpret a p-value, you must know every detail about the assumptions and null hypothesis. If that info’s not available to you, the only valid interpretation of a low p-value is: “Someone was surprised by something.” Let’s all meditate on how little that tells you if you don’t know much about the someone or the something in question.

为了解释p值，您必须了解有关假设和原假设的每个细节。 如果您无法获得该信息，则唯一有效的低p值解释是： “某人对某事感到惊讶。” 让我们一起思考一下，如果您对某人或某事不了解太多，那么该信息将告诉您。

Interpret a low p-value as: “Someone was surprised by something.”

将低p值解释为：“某人对某事感到惊讶。”

Trying to use a p-value to persuade someone is a dangerous bet that your victim is more ignorant than you are. Those who understand what it is might not appreciate your attempt at insulting their intelligence.

试图使用p值说服某人是危险的赌注，即您的受害者比您更无知。 那些了解这是什么的人可能不会欣赏您侮辱他们的智力的尝试。

谢谢阅读！ 喜欢作者吗？ (Thanks for reading! Liked the author?)

If you’re keen to read more of my writing, most of the links in this article take you to my other musings. Can’t choose? Try this one:

如果您希望我的作品，那么本文中的大多数链接都将带您进入我的其他想法。 无法选择？ 试试这个：