Statistics for nursing research: the daily workflow, covered end-to-end

What nursing research statistics actually looks like

Nursing research sits at the intersection of clinical practice and outcomes research. A typical Master's- or DNP-level project asks one of four questions: does this intervention change a patient outcome (pre-post, quasi-experimental, or RCT), does this scale reliably measure what it claims to (validation), do these variables predict this outcome (regression), or are there differences between patient or unit groups (comparison). The analyses are inferential statistics on modest sample sizes — the specialist machinery of CFA/SEM/IRT rarely enters the picture.

Your typical study, mapped

Whatever the project shape, one of the following will almost certainly answer the primary question:

• Pre/post pilot on one group — paired t-test, or Wilcoxon signed-rank when the paired differences aren't normal.
• Two patient groups (control vs intervention) — independent t-test / Welch's t-test / Mann-Whitney U depending on variance and normality.
• Three or more groups — one-way ANOVA (Welch's if variances differ; Kruskal-Wallis if normality fails), with Tukey / Games-Howell / Dunn's post-hoc as appropriate.
• Baseline-adjusted group comparison — ANCOVA, more powerful than a raw t-test whenever there's a meaningful baseline covariate.
• Two categorical variables (e.g. adherence × group) — chi-square, or Fisher's exact for small expected counts.
• Repeated measurements on the same patients — repeated-measures ANOVA (Friedman if non-parametric).
• Predicting a binary outcome (readmission, adverse event) — logistic regression.
• Time to an event (readmission, mortality, wound healing) — Kaplan-Meier + log-rank + Cox regression.
• Scale validation — Cronbach's α with an item-drop table, McDonald's ω, and EFA for dimensionality.
• Inter-rater reliability — Cohen's κ (two raters, categorical) or ICC (multiple raters, continuous).

The workflow, cell by cell

Every one of the analyses above is a first-class test in thericerca. The end-to-end path is the same regardless of which one you need:

1. Upload your dataset — a CSV or Excel export from REDCap, Qualtrics, Google Forms, or a hand-cleaned spreadsheet.
2. Enter the statement of the problem, your objectives, and any hypotheses.
3. thericerca picks the right test for each objective, checks its assumptions on your data, and swaps in the non-parametric alternative when they fail — with a clearly-labelled note about what was swapped and why.
4. Post-hocs (Tukey, Games-Howell, Dunn's) fire only when the omnibus test that earned them was significant, so you never get a comparison licensed by a null result.
5. Download the report in HTML, DOCX, or PDF for your committee, plus a Jupyter notebook (or plain Python script) for reproducibility. Every number in the prose is validated against the underlying data.

When you'd need something else

A concrete example

A DNP capstone piloting a fall-prevention protocol on an inpatient geriatric unit: 60 patients randomised 1:1 to the new protocol vs standard care, followed for 90 days.

• Table 1 — descriptives (age, comorbidities, baseline fall-risk score) and frequencies (sex, mobility level) by group.
• Primary outcome (30-day fall rate) — chi-square with Cramér's V, or Fisher's exact if any expected count < 5.
• Fall-risk score at 30 days — ANCOVA adjusting for baseline score. More powerful than a raw comparison and the modern standard for continuous baseline-covariate designs.
• Nurse satisfaction with the protocol (8-item Likert scale) — Cronbach's α with item-drop, plus a mean-comparison across shifts.
• Time to first fall over 90 days — Kaplan-Meier by group + log-rank test.
• Predictors of any fall (age, baseline risk, group, mobility) — Cox regression, hazard ratios with 95% CIs.

All of that runs in a single thericerca project. Every result lands in an APA-formatted report you can hand your committee, and the reproducibility export gives a reviewer the exact Python code that produced it.