Turn collected data into defensible statistical results
Move from collected data to a documented analysis sequence, diagnostics, sensitivity checks, interpretable results, and clear reporting.
When this support is useful
When data are collected but coding, missingness, model choice, diagnostics, or reporting remain unresolved.
- Is the dataset analysis-ready?
- Which model matches the outcome and dependence structure?
- How should uncertainty be reported?
How the decision is approached
The workflow starts with the research question and data-generating structure. Methods and software follow from that logic, with assumptions, alternatives, and failure modes recorded before conclusions are polished.
- Data-quality and missingness checks
- Regression and outcome models
- Diagnostics and sensitivity analysis
What a defensible handover can include
Deliverables are agreed to fit the decision and the people who must review or continue the work. They are not a fixed bundle of software output.
- Cleaning log
- Analysis code
- Tables, figures, and interpretation memo
Discuss this statistical decision
Bring the research question, design, or existing output. A first review can identify the next statistical decision and the information needed to scope it.