# 3 Designing Experiments: How to design better experiments with David J. Bland

Juicy Patterns Podcast

In this episode, Omer interviews David J. Bland, co-author of the book 'Testing Business Ideas.' They discuss the importance of having an experimental mindset in design and entrepreneurship. David emphasizes the need to be open to the idea of being wrong and to constantly test and adapt based on evidence. They also explore the use of AI in discovery sessions and the balance between data and creativity in design. The conversation covers the role of visual design in experiments, the perception of design work, and the importance of understanding business models. They conclude by discussing how designers can adopt a scientific mindset and translate evidence into insights.

Takeaways

• Having an experimental mindset is crucial in design and entrepreneurship. It involves being open to the idea of being wrong and constantly testing and adapting based on evidence.
• AI can be used in discovery sessions, but it's important to be cautious and not rely solely on AI-generated answers. Evaluative use of AI, such as evaluating interview scripts or business models, can be more valuable than generative use.
• Design sprints can be effective, but it's important to include customer feedback and not make big decisions based on one test. Rapid iteration and learning from multiple experiments over time is key.
• Balancing data and creativity is essential in design. It's important to test multiple directions with low fidelity before investing heavily in high-fidelity designs. Visual design plays a crucial role in building trust and credibility.
• Designers can change the perception of their work by understanding different types of experiments and the strength of evidence they provide. Designers should be aware of the currency they're asking for from customers and design accordingly.
• Thinking like a scientist involves being open-minded, asking clarifying questions, and being willing to challenge your own assumptions. It's important to iterate and learn from each experiment, rather than just going through the motions.