Case Examples from the Health Sciences
If you're considering statistics support, the most useful thing is seeing how the process actually unfolds. Below we walk through the five study types we encounter most in the health sciences — from how the data arrives to the delivered report: a medical specialty thesis, inter-rater agreement in dentistry, a nursing scale study, a pre-post experimental design in psychology, and a journal revision.
The examples are representative cases with details altered for confidentiality; they contain no patient or participant data. Find the one your study resembles, and you'll see what the same journey looks like for you.
Who these examples help
Residents who have collected specialty-thesis data and are at the “now what?” stage
Nursing and psychology researchers running scale adaptation/development studies
Anyone working with an experimental design who needs an analysis plan
Manuscript authors wrestling with a reviewer revision
Case 1 — Medical specialty thesis: retrospective chart review
Situation: A resident wants to compare complication rates of two treatment approaches in a retrospective cohort of ~200 patients. The data came out of the hospital system into Excel with missing fields and inconsistent coding (the same diagnosis spelled three ways). The advisor is at the “show it to statistics” stage.
What we did: The initial review standardized the coding and examined the missing-data pattern — we cleaned the data ourselves rather than sending it back to be fixed. After descriptives, groups were compared with chi-square and Mann-Whitney U; a multivariable logistic regression with age, comorbidity and treatment type modeled predictors of complications, reported as ORs with 95% CIs.
Outcome: A thesis-formatted Table 1, comparison tables and a regression table — every test choice justified in writing. At the defense, the resident answered “why logistic regression?” straight from the report.
Case 2 — Dentistry: inter-rater agreement
Situation: A dental specialty student must report how consistently two observers applied the same classification to panoramic radiographs; the journal also wants intra-rater repeatability.
What we did: Weighted kappa was chosen because the classification is ordinal (standard Cohen's kappa would apply to nominal data); intra-rater agreement compared the same observer's two reading sessions. Kappa values were reported with 95% confidence intervals and Landis-Koch interpretation criteria.
Outcome: A ready agreement-analysis paragraph and table for the methods section. The manuscript passed the statistics review on first submission.
Case 3 — Nursing: scale adaptation study
Situation: A nursing PhD student is testing the validity and reliability of the Turkish form of a scale developed abroad, in a sample of ~300. She arrived with the question “EFA, CFA, or both?”
What we did: Since the original factor structure was known, we started with confirmatory factor analysis (CFA); fit indices (CFI, TLI, RMSEA, SRMR) were reported alongside their thresholds. Modification suggestions were evaluated for borderline items. Reliability was assessed with Cronbach's alpha and item-total correlations, plus inter-subscale correlations.
Outcome: The validity-reliability section, tables included, ready — with the rationale for each reported fit index. The study was accepted by a field journal.
Case 4 — Psychology: pre-post experimental design
Situation: A psychology master's student is examining the effect of an intervention program on anxiety with a treatment-control, pre-post design (~30 per group). The initial plan was “run four t-tests.”
What we did: We explained how multiple t-tests inflate Type I error, and set up the correct model: a 2x2 mixed-design ANOVA (group x time interaction). Normality and homogeneity were checked, the interaction was reported with partial eta-squared, and the significant interaction was unpacked with simple-effects analysis.
Outcome: The intervention's effect reported as a single interaction effect, visualized with a plot. The student got enough Q&A support to explain the design herself at the defense.
Case 5 — Journal revision: ROC and cut-off request
Situation: A research group's manuscript received a major revision; the reviewer wants ROC analysis of a biomarker's diagnostic performance, an optimal cut-off, and multiple-comparison correction. Thirty days to respond.
What we did: The ROC curve was drawn, AUC computed with a 95% CI, and the optimal cut-off determined by Youden index, with sensitivity and specificity at that point. Bonferroni correction was applied to the secondary comparisons. A point-by-point technical response was written for the statistical items of the reviewer letter.
Outcome: The revision closed in a single round; the paper was accepted. The group planned their next study's analysis with us from day one.
What these cases share
None arrived with “ready-made statistics knowledge” — a research question and data were enough; we determined the methods
Every report justifies its test choices in writing — ready for defense and reviewer questions
The analysis itself was usually done in 15 minutes, with reports delivered the same day — within hours, even minutes; total time was set by iterative steps like gaps and corrections
Post-delivery Q&A support carried each project through to defense or acceptance
Frequently asked questions
My study doesn't resemble these examples — can you still help?
Most likely yes. The examples represent our most frequent study types; we work on any academic study with quantitative data, including fields outside health. Describe your study and we'll reply within 24 hours with a free assessment.
Are these cases real clients?
They are representative examples of the study types we encounter often, with details altered for confidentiality. None contain real patient/participant data or identities — and the same confidentiality applies to your study.
What should I prepare before sending my data?
The ideal package: the data file (Excel/SPSS/CSV), your research questions or hypotheses, and your survey form or proposal if available. Missing pieces are fine — we sort them out together during the initial review, fixing what we can ourselves.
What does post-analysis support cover?
You can ask about any method or finding in the report; if your advisor or a reviewer requests additional analyses, we update the work on the same dataset. The goal is getting you ready to stand behind the work on your own.
Let's talk about your case
Describe your study and data — within 24 hours we'll tell you the route we'd take, with a free assessment.
Last updated: July 8, 2026