Statistical problems, explained through research outcomes.
Use this page for permission-cleared client stories or fully anonymized projects. The examples below show the recommended structure.
Choosing the right model for complex research data
Challenge: A researcher had multiple variables, an unclear modelling strategy and uncertainty about assumptions.
Approach: Research-question review, exploratory analysis, model comparison, diagnostics and interpretation.
Deliverables: Reproducible analysis workflow, statistical tables, visualisations and plain-English interpretation.
Outcome: A clearer, defensible analysis plan that could be explained to supervisors and examiners.
Turning a confusing dataset into a clear analysis plan
Challenge: A Master's researcher had collected data but was unsure which statistical tests were appropriate.
Approach: Variable mapping, assumption review, descriptive analysis and test-selection guidance.
Deliverables: Analysis roadmap, output interpretation and research-ready tables.
Outcome: A structured workflow the student could understand and confidently present.
Important: for knowledge/educational purposes