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

这是关于如何教会我评估p值的摘要，希望可以帮助其他一些非统计学家。

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.

让我们从上下文开始。 p值何时生效？ 做出由数据支持的决策很重要。 在数据科学中，这称为数据驱动决策(DDDM)。 收集数据，形成关于数据含义的假设，然后通过一系列统计计算(也称为假设检验)运行数据，最后，您获得的计算值可帮助您评估假设的有效性。 这些计算值之一是p值或概率值。

Hypothesis Testing

假设检验

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.

假设您有关于在城市街道上发现动物的数据。 这些目击者包括狐狸，土狼，小鼠，猫，狗，甚至大象！ 看到大象走在街上的概率是多少？ 就像任何优秀的科学家所做的一样，您会提出一个假设并进行检验。 这称为假设检验。 在假设检验中，您有两个相反的假设。 首先是零假设，它有效地表明这里的数据中没有任何重要的证据，在这种情况下，发现大象的情况并不罕见 。 或者，您有一个假设，该假设基本上说明了研究的目的或您要在计算中测试的内容。 简而言之，替代假设指出有证据表明发生了重大事件，因此您应该拒绝原假设，在这种情况下，很少见到大象，因此是重大事件。 重要程度可能很难定义。 统计人员称其为alpha值。 通常使用0.05的显着性水平或alpha作为显着性阈值，这意味着，如果对数据进行的计算得出的p值小于0.05，则认为结果具有统计学意义。

How do you Interpret the P-value

您如何解释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.

您已经清理了数据，提出了假设，并将数据放入了数据科学魔术的黑匣子中，现在您有了一个p值。 你用它做什么？ p值是在假设零假设为真的情况下获得数据结果概率的度量。 您看到象大象在城市街道上行走一样极端的可能性有多大？ 低的p值(小于0.05的显着性阈值)表明它不太可能发生，因此此类事件的发生非常重要。 较高的p值(例如p值为1)表示该事件很普遍，而不是异常情况。 如果您的样本总体由马戏团成员组成，则可能会得到此值。

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.

很简单，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，而您实际上看到的是大象，那将是一件很重要的事情！ 这意味着很难获得此值，并且不太可能发生它。