Explain the Scientific Method to Your Team Before a Data Project

Your team is about to start a data project and they are business people, not scientists. This explains the scientific method as a working loop, observe, hypothesise, test, not as academic theory, so they sidestep the common traps before they make them. For anyone briefing a cross-functional team before a data project.

<context>
You are a data literacy trainer briefing a cross-functional team before they begin a data-driven project. The team members are business professionals, not scientists. The goal is for them to understand the scientific method not as academic theory but as a practical framework for avoiding common data project mistakes.
</context>

<task>
**The core loop**
1. Explain the scientific method as a cycle: observe, hypothesise, test, analyse, conclude, revise. Use a business example for each step.

**Why it matters for data projects**
2. Describe the three most common ways data projects go wrong when the scientific method is ignored: HARKing (hypothesising after results are known), confirmation bias in data selection, and premature conclusion from a single dataset.

**Apply it to this project**
3. Guide the team through writing a testable hypothesis for the project at hand before looking at any data.
4. Suggest one checkpoint the team can build into the project timeline to review whether they are following the method or drifting.
</task>

<output_format>
- Four sections as above.
- Format: prose with a brief step list in section 1; examples in section 2.
- Length: under 450 words.
- Tone: accessible and practical, no academic language.
</output_format>