Before you start: (a) define one dimensionless target metric and its range; (b) write down the specific variables you plan to measure; (c) ask: with only these variables, is the claimed accuracy realistic—or are key, independent effects missing? If uncertain, add one clearly independent variable or rethink the setup; if not, proceed. Let’s build an RG thread of short templates: task → variables measured → promised accuracy → result → was the plan realistic? The aim is practical: fewer costly studies that were unlikely to succeed from the start, and faster launches for the ones that can.
Avoiding “empty” experiments starts with structured planning. A simple pre-study checklist can save time, resources, and frustration:
1. Define a clear target metric:
- Choose one dimensionless metric or primary outcome.
- Specify the expected range or precision.
2. List all variables to be measured:
- Include only variables that are directly relevant to your hypothesis.
- Ensure that each variable contributes meaningful information to the target metric.
3. Assess completeness of your design:
- Ask: With these variables, is the claimed accuracy realistic?
- If key effects or independent variables are missing, add at least one independent variable or reconsider the experiment.
4. Evaluate feasibility:
- Consider measurement limitations, sample size, and variability.
- Check whether results can be interpreted reliably with the planned setup.
5. Document decisions:
- Record your task, variables, expected accuracy, and rationale.
- Use this as a template for future experiments to reduce unproductive studies.
References / Further Reading:
- Box, G.E.P., Hunter, W.G., & Hunter, J.S. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.
- Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.
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