p是概率嗎？ (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.

從技術上講， p值代表概率值 ，但是由于所有統計數據都涉及概率決策，因此 ，這可能是我們可以給它提供的最不實用的名稱。

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

相反，這里有一些更有趣的候選名稱供您娛樂。

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.

痛苦的價值：他們使您無法在課堂上正確地進行計算； 難怪你的大腦受傷了。 此類別中的榮譽提交還包括令人困惑的價值 ， 困惑的價值和懲罰性的價值 。

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!”

討厭的價值/有問題的價值：統計學家對看到無用的濫用p值感到厭倦，以致于有些人希望看到 p值被廢除 。 他們希望他們能打動人們，大喊：“這是個人決策的工具，而不是您認為的其他東西！”

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?

有說服力的價值：正如我稍后會解釋的那樣，嘗試使用p值說服某人是一個危險的賭注，認為您的受害者比您更無知。 如果您打算使用p值來增加信息的趣味性，我是否建議您在使用拉丁語時重寫所有參數？

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.

可發布的價值：說到濫用p值的方式，如果您是那些不會scientists悔折磨(“ p hacking”)數據直到承認自己認為會令人印象深刻的p值的“ 科學家 ”之一，學術期刊的審稿人，您是問題的一部分，而不是解決方案。

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!

薪水價值：如果您認為學術界是薪水取決于您制定好看的p值的能力的唯一地方，請再考慮一遍！

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.

關鍵點價值：經典的統計推斷歸結為： “我們收集的證據是否使原假設變得荒謬？” p值是重點，將這個大測試問題的答案歸納為一個小數目。

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，您的證據在宇宙中的可信度就越高，在該宇宙中我們并非完全不愿意采取默認行動 。 請注意，這是關于您的證據在特定世界中的合理性……而不是該世界本身的合理性！

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.)

被動值： p值越高，改變主意的原因就越少。 繼續做您打算做的事。 要了解原因，請繼續閱讀。 (但是請記住， 缺乏證據是不一樣的東西缺乏證據 。一個無聲的煙霧報警器并不總是意味著沒有火災。)

P是打Kong！ (P is for Punchline!)

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

還記得我們如何將統計推斷簡化為一個句子嗎？ 它是：

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

我們收集的證據是否使我們的零假設看起來很荒謬？

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值是該問題的重點。 它總結了幾個答案。 p值越低，原假設看起來就越荒謬！

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，我們如何將答案變成是或否？ 我們只是簡單地預先設置一個閾值，以表明什么足以改變我們的想法的荒謬。 該閾值的奇特名稱是顯著性水平 。 如果p值低于該值，請改變主意。 如果沒有，默認情況下繼續做自己喜歡做的??事情。

*是*與*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.

關于p值的一個奇妙之處在于，它們易于使用且相對安全...如果您為無效假設和假設選擇了正確的檢驗，則可以使用。 (如果這么大的話！)但是請不要忘記，您剛剛學到的是他們在做什么 ，而不是他們在 做什么。

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

不要試圖去理解它們在一個簡單的單行代碼中的錯誤。

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.

他們都是奇怪的事情：有關特定假想的宇宙樣品的概率聲明。 它們絕對不是您希望它們成為的簡單明了的東西； 他們的目的不是要直觀地解釋或難以描述。 它們用于讀取假設檢驗的輸出。

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 要看到這一點，您需要了解我們如何計算它們。 我已經在其他文章(例如here)中對此進行了介紹，因此在這里我將堅持摘要。

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

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

計算p值是一個五步過程。

1. Choose the default action.

   選擇默認操作 。

2. State the null hypothesis.

   陳述原假設 。

3. State the assumptions about how the world described by that null hypothesis works.
   陳述有關該原假設所描述的世界如何工作的假設。
4. Make a model of that world (using equations or simulation) — this is the bulk of the work for statisticians.
   創建該世界的模型(使用方程式或模擬)-這是統計學家的主要工作。
5. Find the probability that this world coughs up evidence at least as bad as we’re seeing in our real-life data.
   找到這個世界咳嗽證據的可能性至少與我們在現實生活數據中看到的一樣糟糕。

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

1. Compare it against the significance level.

   將其與顯著性水平進行比較。

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值低于顯著性水平，請改變主意。 否則，只要您從未分析過任何數據，就繼續做您打算做的事情。

摘要：簡短說明 (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.

一個p值詢問： “如果我生活在應該采取 默認行動 的世界中 ，我的證據有多令人驚訝？” P值越高，我堅持執行計劃中的動作就越可笑。 如果p值足夠低，我會改變主意并做其他事情。

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?

政治價值/極化價值： 如果您想了解 p值的爭議 并閱讀我對p值引起的所有情緒的看法，請查看本系列的下一篇文章： 為什么p值像針一樣？

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.

為了解釋p值，您必須了解有關假設和原假設的每個細節。 如果您無法獲得該信息，則唯一有效的低p值解釋是： “某人對某事感到驚訝。” 讓我們一起思考一下，如果您對某人或某事不了解太多，那么該信息將告訴您。

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

將低p值解釋為：“某人對某事感到驚訝。”

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.

試圖使用p值說服某人是危險的賭注，即您的受害者比您更無知。 那些了解這是什么的人可能不會欣賞您侮辱他們的智力的嘗試。

謝謝閱讀！ 喜歡作者嗎？ (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，那么本文中的大多數鏈接都將帶您進入我的其他想法。 無法選擇？ 試試這個：