Craft Effective Questions - SMART Questions

Introduction to Effective Questions

• Importance of questioning in data analysis.

• Constantly asking questions is essential for clarity and insight.

• Questions help clarify project plans and resolve conflicts in data interpretations.

Types of Questions

Leading Questions

• Example: "These are the best sandwiches ever, aren't they?"

• Leading questions steer responses in a certain direction, limiting unbiased opinions.

Closed-Ended Questions

• Example: "Did you enjoy growing up in Malaysia?"

• Closed-ended questions can be answered with yes or no, which limits the depth of insight gained.

Vague Questions

• Example: "Do you prefer chocolate or vanilla?"

• Lack of specificity and context leads to ambiguity in responses (e.g., ice cream vs. coffee flavor).

Importance of Asking the Right Questions

• The ‘ask’ phase is crucial in the data analyst process.

• Understanding effective vs. ineffective questioning styles is essential.

SMART Methodology for Effective Questions

Specific Questions

• Specific questions are focused on a single topic.

• Example: Instead of asking about children's physical activities generically, ask, "What percentage of kids achieve the recommended sixty minutes of physical activity at least five days a week?"

Measurable Questions

• Measurable questions can be quantified.

• Example: Instead of asking, "Why did a recent video go viral?" ask, "How many times was our video shared in the first week?"

Action-Oriented Questions

• Encourage change and actionable insights.

• Example: Instead of asking, "How can we get customers to recycle our product packaging?" ask, "What design features will make our packaging easier to recycle?"

Relevant Questions

• Relevant questions hold significance for the problem at hand.

• Example: For a species at risk, a better question would be, "What environmental factors changed in Durham, NC, between 1983 and 02/2004 that could explain the decline of Pine Barrens tree frogs?"

Time Bound Questions

• Specify the time frame for data collection.

• Example: Focus on the period between 1983 and 02/2004 for environmental questions pertaining to specific species.

Fairness in Question Crafting

• Fairness prevents bias in questioning.

• Example: Phrasing questions to avoid leading or assuming an answer, such as asking "What do you love most about our exhibits?" which assumes all visitors love the exhibits.

• Clear and straightforward wording ensures everyone understands the questions.

• Unfair questions lead to unreliable feedback and hinder valuable insights.

Conclusion

• Mastering the art of asking the right questions is critical for effective data analysis.

• The next phase involves exploring data types and their applications in guiding business decisions, along with the introduction to visualizations and metrics.