#data science.

You are a biostatistician and data scientist. I have run a [type of analysis: regression/ANOVA/t-test/chi-square/etc.] and need help interpreting the results. Think step by step: 1) State what statistical test was run and what it is designed to test, 2) Interpret the key output metrics (coefficient/p-value/F-statistic/etc.) in plain English — what does each number mean?, 3) Is the result statistically significant? Is it practically significant? (Distinguish between these.), 4) What are the key assumptions of this test and should I check if they are violated in my data?, 5) What can I conclude from this analysis? What should I NOT conclude (common misinterpretation)?, 6) What follow-up analysis would strengthen or challenge this finding? Results: [paste your output]. Context: [describe your research question and dataset].

You are a senior data scientist. I have a dataset described as: [describe columns, row count, time range, source, and the business question]. Create a structured EDA (Exploratory Data Analysis) plan. The plan should include: 1) Data Quality Checklist — what to check first (missing values, duplicates, data type issues, outlier detection approach), 2) Univariate Analysis — which variables to examine first and why, 3) Bivariate Analysis — the 5 most important variable relationships to investigate given the business question, 4) Segmentation Analysis — are there natural groups in this data that should be analyzed separately?, 5) Hypothesis List — 5 hypotheses to test before building any model, 6) Visualization Plan — for each analysis, the best chart type and what a "surprising" vs "expected" finding would look like. Also provide: the Python/R code structure for steps 1-3.

You are a Staff Machine Learning Engineer. Design a production ML system for the following problem: [describe the business problem, e.g., "predict customer churn 30 days in advance"]. Deliverables: 1) Problem Formulation — reframe the business problem as an ML problem (classification/regression/ranking/generation?), define the prediction target precisely, 2) Data Requirements — what data is needed, where it comes from, what quality issues to expect, 3) Feature Engineering Plan — 10 candidate features with rationale; identify target leakage risks, 4) Model Selection — evaluate 3 candidate algorithms; recommend one with justification, 5) Training Infrastructure — compute requirements, training frequency, retraining triggers, 6) Evaluation Framework — the right metric for this problem (not just accuracy), offline vs online evaluation, a baseline to beat, 7) Deployment Architecture — batch vs real-time serving, A/B test design for model rollout, 8) Monitoring Plan — data drift, model drift, business metric correlation, 9) Failure Modes — what goes wrong when the model is confidently wrong?