Magnus Hedemark
487f8923dc
PhD-level data science expertise with decision framework, five reference documents (statistical methodology, experimental design, causal inference, regression modeling, Bayesian workflow), five automation scripts (power analysis, assumption diagnostics, model comparison, effect size calculator, experimental design generator), and two report templates. Python default with --engine r flag for R output. Dual language support.
3.6 KiB
Analysis Report Template
1. Question & Context
Research question: [One sentence: what are we trying to learn?]
Motivation: [Why does this question matter? What decision depends on the answer?]
Data source: [Where did the data come from? Collection method, timeframe, sample frame]
Pre-registration: [Link to preregistration if applicable. If not, flag any exploratory analyses.]
2. Data
Sample size: N = [n] ([n1] in group 1, [n2] in group 2)
Inclusion/exclusion criteria: [Who/what was included and excluded, and why]
Missing data: [Amount, pattern (MCAR/MAR/MNAR), handling method]
Variables:
| Variable | Type | Role | Description |
|---|---|---|---|
| [name] | [continuous/binary/ordinal/etc.] | [outcome/predictor/covariate] | [description] |
| ... |
3. Methods
Analytic approach: [e.g., two-sample t-test, linear regression with covariates, Bayesian hierarchical model]
Justification: [Why this method? What assumptions are we willing to make?]
Pre-specified analyses: [What was planned before seeing the data]
Exploratory analyses: [What was added after seeing the data]
Software: [Python 3.x with scipy/statsmodels/scikit-learn, R 4.x with package vX]
4. Assumption Checks
| Assumption | Method | Result | Status |
|---|---|---|---|
| Normality (Group 1) | Shapiro-Wilk | W = [value], p = [value] | ✓ / ✗ / N/A |
| Equal variance | Levene's test | F = [value], p = [value] | ✓ / ✗ / N/A |
| ... |
Summary: [All assumptions met / violations detected and addressed]
5. Results
Primary analysis:
| Estimate | SE | 95% CI | Test Statistic | p-value | Effect Size [95% CI] |
|---|---|---|---|---|---|
| [value] | [value] | [lower, upper] | [t/χ²/F/Z = value] | [value] | [d/η²/V/OR = value [CI]] |
Secondary analyses:
[Brief summary of secondary results]
Visualization:
[Figure: appropriate plot with clear axes, uncertainty shown, caption below]
Figure 1: [Caption describing what the reader should see]
6. Diagnostics & Robustness
Sensitivity analyses:
| Analysis | Result | Conclusion |
|---|---|---|
| Main analysis | [original estimate] | — |
| Excluding outliers | [estimate] | Consistent / different |
| Alternative specification | [estimate] | Robust / sensitive |
| Different analysis method | [estimate] | Robust / sensitive |
Residual diagnostics: [Pattern in residuals? Influential points?]
7. Limitations
- [Assumption that may be violated]: [How this could affect results]
- [Confounding not addressed]: [Direction and magnitude of potential bias]
- [Generalizability concern]: [Population or setting limits]
- [Measurement issue]: [Reliability, validity of measures]
8. Conclusion
[One paragraph answering the original question, with appropriate uncertainty. Include:
- What we found (with effect size and precision)
- What we didn't find (null results with equivalence if applicable)
- What remains uncertain
- Practical implications
Example: "We found moderate evidence that the intervention increases response rate by 12 percentage points (95% CI [4, 20], p = 0.003, d = 0.45). This effect was robust to excluding outliers and controlling for baseline covariates. However, the result may not generalize to non-English-speaking populations, and the mechanism remains unclear."]
Appendix
Full model output: [Link or table]
Code: [Link to repository]
Data: [Access information or note about availability]