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Definition

Questioning in data science is the process of asking thoughtful and insightful questions about data in order to gain new insights and knowledge. It is a critical skill for data scientists, as it allows them to identify and solve problems, make better decisions, and communicate their findings effectively.

Effective questioning in data science involves the following steps:

1. Identify the problem or opportunity. What are you trying to understand or achieve with your data analysis? Once you have a clear understanding of the problem or opportunity, you can start to develop specific questions.
2. Ask open-ended questions. Open-ended questions encourage exploration and discovery. They allow you to gather more information and to generate new hypotheses.
3. Ask specific questions. Specific questions help you to drill down into the data and to get more precise answers. They are also useful for testing hypotheses.
4. Ask challenging questions. Don’t be afraid to ask challenging questions. These questions can lead to the most innovative and insightful discoveries.

Here are some examples of questions that data scientists might ask:

• What are the trends in the data?
• What are the relationships between the different variables in the data?
• What are the outliers in the data?
• What are the drivers of change in the data?
• What are the implications of the data for the business?

By asking thoughtful and insightful questions, data scientists can gain valuable insights from their data and make a significant impact on their organizations.

Here are some tips for effective questioning in data science:

• Be clear about your goals. What do you want to achieve by asking this question?
• Do your research. Before you ask a question, take some time to learn about the data and the problem or opportunity that you are trying to understand.
• Be specific. Avoid asking vague or general questions. Instead, focus on asking specific questions that will help you to achieve your goals.
• Be open-minded. Be willing to consider all possible answers to your questions, even if they contradict your current beliefs.
• Be persistent. Don’t give up if you don’t get an answer right away. Keep asking questions until you have a clear understanding of the problem or opportunity.

By following these tips, you can improve your questioning skills and become a more effective data scientist.

Be careful at the misleading questions too!

Misleading questions in data science are questions that are designed to lead the audience to a particular conclusion, or to obscure important information. They can be categorized into two main types:

• Biased questions: These questions are designed to favor a particular outcome. For example, a company might ask “What are the benefits of our new product?” instead of asking “What are the benefits and drawbacks of our new product?”
• Loaded questions: These questions assume a certain fact or conclusion to be true. For example, a company might ask “How much money will we make from our new product launch?” instead of asking “Is it likely that our new product will be successful?”

Other types of misleading questions in data science include:

• Leading questions: These questions suggest the answer that the questioner wants to hear. For example, a researcher might ask “Do you agree that our new drug is safe and effective?” instead of asking “What are the benefits and risks of our new drug?”
• Double-barreled questions: These questions ask two questions at once, making it difficult to give a meaningful answer. For example, a survey might ask “How satisfied are you with the customer service and the product selection?” instead of asking “How satisfied are you with the customer service?” and “How satisfied are you with the product selection?”
• Vague questions: These questions are not specific enough to be answered accurately. For example, a manager might ask “What can we do to improve our sales?” instead of asking “What specific strategies can we implement to increase sales of our new product?”

Data scientists should be aware of the different types of misleading questions and be able to identify them. By being aware of these questions, data scientists can avoid being misled and produce more objective and reliable results.

Here are some tips for identifying misleading questions in data science:

• Look for bias. Is the question designed to favor a particular outcome?
• Look for assumptions. Does the question assume a certain fact or conclusion to be true?
• Look for leading statements. Does the question suggest the answer that the questioner wants to hear?
• Look for double-barreled questions. Does the question ask two questions at once?
• Look for vague questions. Is the question specific enough to be answered accurately?

If you identify a question as being misleading, you can ask the person asking the question to clarify their intent, or you can simply refuse to answer the question.