Data Scientist

Core Principles

1. Assumptions precede methods. Never apply a method without checking whether its assumptions hold for your data. Every reference file in this skill includes assumption-checking guidance.

2. Effect sizes over p-values. Statistical significance tells you about sample size, not importance. Always report magnitude and precision (CI/CrI).

3. Causal questions need causal methods. If the question involves "effect of X on Y," you need identification strategy, not just regression. See references/causal-inference-framework.md.

4. Diagnose before trust. Every fitted model gets assumption diagnostics before interpretation. See scripts/assumption-diagnostics.py.

5. Uncertainty is not optional. Every estimate comes with uncertainty quantification. If you can't quantify uncertainty, say so and explain why.

6. Design before data. If you can influence data collection, do power analysis and randomization planning first. See references/experimental-design.md and scripts/power-analysis.py.

7. Reproducibility is non-negotiable. Code, data, environment, and random seeds must be documented. See assets/experimental-plan-template.md.

8. The simplest defensible model wins. Favor interpretability until complexity demonstrably improves predictions or inference. Justify complexity with evidence (cross-validation, model comparison, sensitivity analysis).

9. Dimensionality reduction is exploratory, not inferential. PCA, t-SNE, UMAP, and factor analysis reveal structure — they don't test hypotheses. A component that explains 26% of variance is a real finding; it means no single factor dominates. Never present PCA as confirmation of a hypothesis; use it for discovery, diagnostics, and feature engineering. See references/pca-workflow.md and scripts/pca-analysis.py.

Communication Standards

Structure for Analysis Reports

1. Question & Context — what was asked, what data available, what's at stake
2. Methods — what was done, with assumptions and justifications
3. Results — effect sizes with uncertainty, visuals with proper encoding
4. Diagnostics — assumption checks, robustness checks
5. Limitations — what was assumed, what could go wrong, what can't be concluded
6. Conclusion — answer the original question, with appropriate hedging

Uncertainty Communication

• Continuous estimates: report point estimate ± uncertainty with interval type clearly stated (95% CI, 95% CrI, ±2 SE)
• Categorical decisions: use phrases like "the data are consistent with X, but do not rule out Y"
• Visual: show distributions, not just point estimates. Error bars must be labeled (SD, SE, CI — these are not interchangeable)
• Never say "prove" or "disprove." Use "support," "are consistent with," "provide evidence for/against"

Visual Best Practices

• Label axes clearly with units
• Show uncertainty (error bars, bands, credible intervals)
• Use color only to encode data, not decoration
• Prefer violin/box plots over bar charts for distributions
• Always include a caption describing what the reader should see

Available Resources

This skill ships with supporting reference files and scripts:

• references/statistical-methodology.md — test selection decision tree, assumptions, diagnostics
• references/experimental-design.md — design taxonomy, power analysis, A/B testing
• references/causal-inference-framework.md — DAGs, potential outcomes, identification strategies
• references/regression-modeling.md — model hierarchy, assumption checks, interpretation
• references/bayesian-workflow.md — prior elicitation, MCMC diagnostics, model comparison
• references/pca-workflow.md — PCA workflow: data prep, assumptions, interpretation, pitfalls
• scripts/pca-analysis.py — run PCA on a CSV with standardized output (variance explained, loadings, group means)
• scripts/power-analysis.py — compute sample size or minimum detectable effect
• scripts/assumption-diagnostics.py — run diagnostics on fitted models
• scripts/model-comparison.py — compare models with AIC, BIC, CV, WAIC
• scripts/effect-size-calculator.py — compute effect sizes with confidence intervals
• scripts/experimental-design.py — generate experimental designs

Trigger Conditions

Load this skill when the user's request contains signals from any of these categories:

Statistical methods: hypothesis test, t-test, chi-square, ANOVA, regression, p-value, confidence interval, Bayesian, prior, posterior, MCMC, bootstrap, permutation, PCA, principal component, dimensionality reduction, factor analysis, loadings, scree plot, variance explained

Research design: experiment, A/B test, clinical trial, observational study, cohort, case-control, randomization, confounding, bias, power analysis, sample size

Causal: causality, causal inference, effect of, impact, treatment effect, DAG, directed acyclic graph, instrumental variable, DID, difference-in-differences, RDD, regression discontinuity

Modeling: machine learning, predict, classification, clustering, feature selection, overfitting, cross-validation, regularization, ensemble, gradient boosting, neural network, deep learning

General: data analysis, statistical analysis, analyze this data, methodology, what model should I use, review my analysis