Module 1 — Questions, Data & Cleaning
From statistical question framing to data dictionaries, cleaning logs, and missingness audits.
Statistical question formation
Academic anchor: before any chart or cleaning decision, students must turn a vague topic into a researchable statistical question by naming the population, observational unit, variables, time frame, and inference boundary.
The statistical question formation builder needs JavaScript.
Interactive reading
A student asks: “Does screen time affect grades?” Commit to an answer before you open this: what is missing before it becomes a defensible statistical question?
Question triage lab
Visualization begins with a statistical question, not a chart type. Sort each statement into what it actually is — and notice how often a causal verb sneaks into a design that cannot support it.
The question triage lab needs JavaScript.
Types of data interactive lab
Before choosing a visualization, classify the variable correctly. The data type determines what summaries are legal, which chart encodings are honest, and what an AI-generated claim is allowed to say. Then build the data dictionary yourself — it is the contract every later chart in your capstone has to satisfy.
The data-types lab needs JavaScript.
Dirty-data audit
A real extract arrives with inconsistent labels, missing values, duplicates, implausible entries, and unit errors mixed together. Diagnose each row before repairing it: the wrong diagnosis produces the wrong repair, and a deleted row is evidence you can never get back.
The dirty-data audit needs JavaScript.
AI-output audit
An assistant proposes a six-step cleaning protocol. Rule on every step and build the “AI suggestions accepted / rejected” table that ships with your data dictionary and cleaning log.
The AI cleaning-log audit needs JavaScript.
Hands-on visual lab
The assistant claimed that dropping every incomplete row always improves accuracy. Test that claim directly: hold the data fixed, change only the reason values go missing, and watch the complete-case estimate drift away from the truth.
The missingness explorer needs JavaScript.
R lab
Import a small CSV, inspect glimpse(), create a data dictionary, and summarize missingness by variable.
# Suggested R workflow scaffold
library(tidyverse)
# 1. Import or construct the dataset
# 2. Verify summaries before visualizing
# 3. Build the ggplot2 visualization
# 4. Write the statistical interpretation and limitation note
# 5. Log any AI assistance and correctionsModule 1 Project / Assignment
This assignment turns the Module 1 labs into a capstone-ready evidence packet. Students choose or receive a small dataset, refine one statistical question, classify the variables, audit the data quality, and document which AI cleaning suggestions were accepted or rejected.
Required submission
- Refined statistical question: state the population, observational unit, measured variables, time frame, and inference boundary.
- Data dictionary: list each variable, data type, measurement level, units, legal values, and any derived-rate definitions.
- Dirty-data audit table: identify missing values, duplicates, inconsistent coding, implausible values, unit mismatches, and genuine outliers.
- Missingness summary from R/WebR: report missing counts by variable and explain whether complete-case deletion is defensible.
- AI accepted/rejected table: paste the AI cleaning recommendation, mark each step accepted or rejected, and justify the decision with evidence.
- Cleaning decision defense: write 150–250 words explaining the final cleaning plan, what evidence was preserved, and what limitation remains.
Mastery evidence: the final packet should show that the student can move from a vague topic to a defensible question, from raw variables to a usable data dictionary, and from AI-generated suggestions to verified cleaning decisions.
Adaptive reflection
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