“Beware of the HiPPO in the room” — The risks and dangers of top-down, intuition-based decision making are well known in the business world. Experimentation and data-based decision making become widely acknowledged as the right way to steer a business.

“当心机房中的HiPPO” —自上而下，基于直觉的决策制定的风险和危险在商业界众所周知。 实验和基于数据的决策被公认为是指导业务的正确方法。

For a good reason: Leading experimenters such as Netflix, Google and Booking show that making decisions based on facts and evidence rather than intuition can lead to exceptional business success.

有一个很好的理由：Netflix，Google和Booking等领先的实验者表明，根据事实和证据而不是凭直觉做出决策可以带来非凡的业务成功。

But what if in the course of this development the HiPPO (Highest Paid Person’s Opinion) is not the one to be afraid of anymore? What if the person that should help to fight top-down decision making took his place?

但是，如果在这种发展过程中，HiPPO(最高付费人士的意见)不再是一个令人恐惧的东西呢？ 如果应该帮助反对自上而下的决策的人接任该怎么办？

What if the analyst is the new biasing factor in decision making?

如果分析师是决策中的新偏见因素怎么办？

Let me be clear. Having personal opinions about new ideas, suggestions and approaches couldn’t be more natural. We all have our cognitive biases. But we can not ignore this fact just because of the allegedly safe framework of evidence-based decision making.

让我清楚一点。 对新想法，建议和方法有个人见解不会再自然。 我们都有我们的认知偏见。 但是，我们不能仅仅因为所谓的基于证据的决策安全框架而忽视这一事实。

偏差分析结果 (Biasing Analytics Results)

The analyst’s responsibility is to paint a clear picture of the business’ situation and inform decision-making. And there are plenty of ways how an analyst can, willingly or entirely unaware, bias the final decision that is made. I like to split those into conscious and unconscious biases.

分析师的责任是清楚地描述业务情况并为决策提供依据。 分析师可以通过多种方式自愿或完全不了解最终决策。 我喜欢将这些分为有意识的和无意识的偏见。

自觉偏见 (Conscious Biases)

Let’s have a look at the first kind, where analysts make deliberate decisions that will impact their results. Conscious biases are closely connected to the analyst’s personal opinion. This can be about a new marketing campaign or a new product feature. Whether the analyst believes in a specific idea can significantly impact how the following research is being conducted. And if it is conducted at all. Let’s have a look at a few potential sources for conscious biases:

让我们看一下第一种类型，在这种类型中，分析师做出会影响其结果的深思熟虑的决策。 有意识的偏见与分析师的个人看法紧密相关。 这可能与新的营销活动或新的产品功能有关。 分析人员是否相信特定想法会严重影响以下研究的进行方式。 如果是进行的话。 让我们看一下一些有意识的偏见的潜在来源：

“Can you just give us a rough estimate for this particular metric?”

“您能给我们这个特定指标的大概估算吗？”

a) Making Guesses. Questions for estimates and opinions are more or less an invitation for introducing biases. Obviously you can’t know all the numbers by heart and the best thing to do would be to go back to your desk, check the metrics and report them back. But checking every single metric costs too much time. Often enough, we simply trust our intuition, which strongly correlates with our personal opinion about a specific idea. So you make an educated guess about what the number might be. At this point, analysts can already substantially impact whether an initiative is pursued and what everybody’s expectations are. The first number one comes up with serves as an anchor figure and sets expectations stakeholders might reference in the future to assess an idea’s potential.

a)猜测。 有关估计和意见的问题或多或少地引起了人们引入偏见。 显然，您不能一味地知道所有数字，而最好的办法是回到办公桌前，查看指标​​并将其报告回来。 但是检查每个指标会花费太多时间。 通常，我们只是相信我们的直觉，这与我们对特定想法的个人看法紧密相关。 因此，您可以对数字可能进行合理的猜测。 在这一点上，分析人员已经可以对是否采取主动行动以及每个人的期望产生实质性影响。 第一个数字一个想出了作为锚人物和套的预期利益相关者在未来可能会引用到评估一个想法的潜力。

“Traffic is so low on this page, it’s not worth looking further into this.”

“此页面上的流量如此之低，因此不值得进一步研究。”

b) Giving Personal Opinions. Sometimes we might be tempted to provide no number at all and instead give a personal opinion. While this opinion is (hopefully) based on facts and the analyst’s experience, it can still strongly correlate with one’s subjective opinion about the idea discussed.

b)发表个人意见。 有时我们可能会不愿提供任何电话号码，而是发表个人意见。 尽管这种观点(希望)基于事实和分析师的经验，但仍可以与人们对所讨论想法的主观观点密切相关。

c) Depth of Research. After kicking off the research, the question is:

c)研究深度。 在开始研究之后，问题是：

When does an analyst have enough information to give a good recommendation or overview for a particular problem?

分析师何时有足够的信息为特定问题给出好的建议或概述？

Of course, you can always drill deeper into a specific topic to get more evidence to support a decision-making process. Analysts might be inclined to dig deeper into an area to prove or disprove a particular idea they have a strong opinion about. Simultaneously, we might put less effort into a research question where the outcome is expected to be less exciting or the decision that has to be made seems to be pretty trivial anyway.

当然，您总是可以更深入地研究特定主题，以获取更多证据来支持决策过程。 分析师可能倾向于更深入地研究某个领域，以证明或反对他们有强烈看法的特定想法。 同时，我们可能会在研究问题上投入更少的精力，因为预期结果不会那么令人兴奋，或者必须做出的决定似乎微不足道。

d) Setting targets. The analytics and experimentation landscape itself invites analysts and anybody who operates in it to introduce biases at some points. Be it setting the right significance or power level for an AB-test or selecting an appropriate metric to measure a new feature or a campaign’s success? Those are, to a certain degree, subjective decisions the analyst has to make to produce any results. But at the same time, those can have a significant effect on the actual outcome of the research.

d)设定目标。 分析和实验环境本身会邀请分析师和其中的任何人在某些时候引入偏见。 是为AB测试设置正确的重要性或功率级别，还是选择适当的度量标准来衡量新功能或活动的成功？ 在某种程度上，这些是分析人员必须做出的主观决定才能产生任何结果。 但是同时，这些可能会对研究的实际结果产生重大影响。

无意识的偏见 (Unconscious Biases)

Unconscious biases are not introduced by the analyst’s active decision making. This sort of bias is less connected to the personal opinion about a specific idea or research question but can have the same magnitude of impact on the results. Biases that fall into this category are for example:

分析人员的主动决策不会引入无意识的偏见。 这种偏见与对特定想法或研究问题的个人看法联系较少，但对结果的影响程度相同。 属于此类的偏差例如：

a) Programming Errors. Be it an error in a SQL-query, a wrong logic statement when filtering a pandas dataframe or an incorrect regex expression. All these programming errors can occur when we’re trying to get insights from the data in front of us. Other than syntax errors, this kind of programming error can remain wholly unnoticed when running our code and thus can have a substantial impact on the results of our analysis.

a)编程错误。 是SQL查询中的错误，过滤熊猫数据框时的错误逻辑语句还是不正确的正则表达式。 当我们试图从眼前的数据中获取洞察力时，所有这些编程错误都可能发生。 除了语法错误外，在运行我们的代码时，这种编程错误可能仍然完全未被注意到，从而可能对我们的分析结果产生重大影响。

b) Wrong handling of data. Usually, the data we want to examine to answer a particular research question does not come in a usable format. Before we can use a statistical model to derive insights from our data, we might have to clean it, select and engineer appropriate features, and eventually perform data transformations. All those actions can bias our dataset and thus our decisions in one direction or another.

b)错误处理数据。 通常，我们要检查以回答特定研究问题的数据不是可用的格式。 在使用统计模型从数据中获取见解之前，我们可能必须清理数据，选择和设计适当的功能，并最终执行数据转换。 所有这些动作都会使我们的数据集产生偏差，从而使我们的决策朝着一个方向或另一个方向倾斜。

c) Wrong interpretation of data. We might have done everything done when handling and modelling our dataset. But in the end, we can still derive the wrong conclusions from the results in front of us. Classic misinterpretations are confusing correlation with causation or drawing the wrong conclusions about the relationship of two parameters.

c)数据解释错误。 在处理和建模数据集时，我们可能已经完成了所有工作。 但是最后，我们仍然可以从我们面前的结果中得出错误的结论。 经典的误解使因果关系变得混乱，或者对两个参数之间的关系得出了错误的结论。

那么分析师是不值得信任的吗？ (So is the analyst to be mistrusted?)

Today’s most valuable companies such as Netflix, Amazon and Google show that experimentation and data have to replace intuition as a basis for making decisions.

如今，诸如Netflix，Amazon和Google之类的最有价值的公司表明，实验和数据必须取代直觉作为决策的基础。

Hence having trust in the data and therefore the analyst’s output is essential. It’s the analyst’s responsibility to build and maintain that trust. Analysts have to do their best to provide unbiased, informative insights to support decision-making and drive businesses in the right direction.

因此，对数据以及对分析人员的输出的信任至关重要。 建立和维护这种信任是分析师的责任。 分析师必须尽力提供​​公正，有用的见解，以支持决策制定并推动业务朝着正确的方向发展。

Therefore, it is imperative to be aware of your own biases and to overcome them where possible.

因此，必须意识到自己的偏见并在可能的情况下克服它们。

When being asked for a rough estimate or when thinking about how deep you want to drill into a specific topic, take a step back. Reflect on your decision and thought process and try to get a neutral perspective on your current issue.

当被要求进行粗略估算或考虑要深入到特定主题的深度时，请后退一步。 反思您的决策和思考过程，并尝试对当前问题持中立观点。

To avoid unconscious biases, it helps to adopt some best practices from the world of software engineering: Use unit tests in your queries and notebooks, start pair programming and ask colleagues to review your code and approach.

为避免无意识的偏见，它有助于采用一些软件工程领域的最佳实践：在查询和笔记本中使用单元测试，开始结对编程，并要求同事审查您的代码和方法。

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