flowchart TD Q[1. Question] --> M[2. Measurements] M --> D[3. Design] D --> A[4. Acquisition] A --> Desc[5. Description] Desc --> An[6. Analysis] An --> I[7. Interpretation / Prediction] I --> V[8. Validation] V --> K[9. Knowledge / Decisions]
The research workflow
APPENDIX · RESEARCH WORKFLOW
The research workflow
Nine stages from a research question to knowledge that survives replication.
Statistics is a thread running through a much longer process. The diagram below names the stages of that process and links each to the parts of the curriculum that cover it in detail. No stage is optional; errors made at one are paid for at the next.
1. Question
A well-formed research question has a population, an exposure or intervention, a comparator, an outcome, and a time frame. The PICO framework from evidence-based medicine is a useful checklist. A vague question leads to a vague design and a vague answer; a well-formed question survives peer review before a single datum is collected. Covered in: Course 1 W1 S1, Course 3 W4 S1.
2. Measurements
Every question depends on the measurement scale you choose to answer it. Accuracy, precision, reliability, and the difference between a measurement and a proxy are the vocabulary. Covered in: Course 1 W1 S3, Course 2 W4 S2.
3. Design
Design is where you choose what the data can and cannot tell you. A well-designed observational study answers some questions better than a badly designed RCT; a good RCT answers them all. Covered in: Course 3 W1.
4. Acquisition
Getting the data into a computer faithfully is itself a statistical problem: missingness, measurement error, and batch effects start here. Covered in: Course 1 W1 S4, Course 3 W2.
5. Description
Before any inference, look at the data. A well-designed plot is often the whole answer. Covered in: Course 1 W1 S5, Course 1 W2 S1.
6. Analysis
The formal model that takes the data and returns an estimate, an interval, and a decision. Covered in: Courses 1–4, throughout.
7. Interpretation / prediction
What does the estimate mean in the scale of the science? Effect size, clinical significance, and the distinction between an average and an individual prediction live here. Covered in: Course 2 W3 S5, Course 4 W3 S5.
8. Validation
Does the finding hold in data not used to find it? External validation, nested CV, and replication pool here. Covered in: Course 4 W1 S1, Course 4 W3 S5.
9. Knowledge / decisions
The act of writing it down so that others can trust, cite, and build on the finding. Covered in: Writing a report, Course 2 W4 S5.