Here is a summary of how I was taught to assess the p-value in hopes of helping some other non-statistician out there.

這是關于如何教會我評估p值的摘要，希望可以幫助其他一些非統計學家。

P-value in Context

上下文中的P值

Let’s start with the context. When does the p-value even come into play? It is important to make decisions that are backed by data. In Data Science, this is called Data-Driven Decision Making (DDDM). Data is collected, hypotheses are formed about what that data means, the data is then run through a series of statistical calculations also known as hypothesis testing, and in the end, you have calculated values that help guide you in assessing the validity of your hypotheses. One of these calculated values is the p-value or probability value.

讓我們從上下文開始。 p值何時生效？ 做出由數據支持的決策很重要。 在數據科學中，這稱為數據驅動決策(DDDM)。 收集數據，形成關于數據含義的假設，然后通過一系列統計計算(也稱為假設檢驗)運行數據，最后，您獲得的計算值可幫助您評估假設的有效性。 這些計算值之一是p值或概率值。

Hypothesis Testing

假設檢驗

Assume you have data on animal sightings in city streets. These sightings include foxes, coyotes, mice, cats, dogs, and even elephants! What is the probability of seeing an elephant walking down the street? As any good scientist does, you develop a hypothesis and test it. This is called hypothesis testing. In hypothesis testing, you have two opposing hypotheses. First is the null hypothesis, which effectively states there’s no evidence of anything significant in the data here, in this case, elephant sightings are not rare. Alternately, you have a hypothesis that essentially states the purpose of the study or what you are testing for in your calculations. Put simply, the alternative hypothesis states there is evidence of a significant event occurring and you should reject the null hypothesis, in this case, sighting an elephant is rare and therefore is a significant event. Significant can be hard to define. Statisticians call it the alpha value. It is typical to use a significance level, or alpha, of 0.05 as the threshold of significance, meaning that if calculations on your data yield a p-value of less than 0.05, the results are considered statistically significant.

假設您有關于在城市街道上發現動物的數據。 這些目擊者包括狐貍，土狼，小鼠，貓，狗，甚至大象！ 看到大象走在街上的概率是多少？ 就像任何優秀的科學家所做的一樣，您會提出一個假設并進行檢驗。 這稱為假設檢驗。 在假設檢驗中，您有兩個相反的假設。 首先是零假設，它有效地表明這里的數據中沒有任何重要的證據，在這種情況下，發現大象的情況并不罕見 。 或者，您有一個假設，該假設基本上說明了研究的目的或您要在計算中測試的內容。 簡而言之，替代假設指出有證據表明發生了重大事件，因此您應該拒絕原假設，在這種情況下，很少見到大象，因此是重大事件。 重要程度可能很難定義。 統計人員稱其為alpha值。 通常使用0.05的顯著性水平或alpha作為顯著性閾值，這意味著，如果對數據進行的計算得出的p值小于0.05，則認為結果具有統計學意義。

How do you Interpret the P-value

您如何解釋P值

You’ve cleaned your data, developed your hypothesis, put the data into the black box of data science magic, and now you have a p-value. What do you do with it? The p-value is a measurement of the probability of obtaining the results in the data assuming that the null hypothesis is true. How likely is it that you see something as extreme as an elephant walking down a city street? A low p-value, less than the 0.05 significance threshold, indicates that it is not very likely and thus the occurrence of such an event is significant. A high p-value, such as a p-value of 1 indicates the event is commonplace and not an unusual occurrence. Perhaps you would get this value if your sample population were comprised of members of a circus.

您已經清理了數據，提出了假設，并將數據放入了數據科學魔術的黑匣子中，現在您有了一個p值。 你用它做什么？ p值是在假設零假設為真的情況下獲得數據結果概率的度量。 您看到象大象在城市街道上行走一樣極端的可能性有多大？ 低的p值(小于0.05的顯著性閾值)表明它不太可能發生，因此此類事件的發生非常重要。 較高的p值(例如p值為1)表示該事件很普遍，而不是異常情況。 如果您的樣本總體由馬戲團成員組成，則可能會得到此值。

Quite simply, the lower the p-value the more significance it holds. If the p-value of seeing an elephant walking down a city street is 0.01 and you do in fact see an elephant, it is a significant event! It means it is rare to get this value and unlikely to be happen-chance that it occurred.

很簡單，p值越低，它的重要性就越大。 如果看到大象在城市街道上行走的p值是0.01，而您實際上看到的是大象，那將是一件很重要的事情！ 這意味著很難獲得此值，并且不太可能發生它。