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Module 7 — Dashboards, Ethics & Misleading Graphics

Weeks 13–14 · graduate statistics · adaptive competencies DV13–DV14

Use Shiny dashboards to support statistical reasoning while auditing denominators, aggregation, bias, privacy, and inference risks.

Statistics focusKPI definitions, aggregation, filters, Simpson’s paradox, sampling bias, privacy, misleading inference

R/tool focusInline browser R labs; optional desktop RStudio/Positron for capstone; Shiny mini-dashboard; Quarto report option; reactive filters with metric definitions

Interactive assignmentDashboard denominator checker, misleading-graphics ethics gallery, Shiny prototype lab

Interactive reading

Predict before reveal

A dashboard shows “completion rate” but never defines the denominator. Why is that a statistical problem?

Rates require clear denominators. “Completion rate” could mean completed among enrolled, active, started, eligible, or assessed students; interpretation changes with the denominator.

Dashboard tile audit

The dashboard tile audit needs JavaScript.

Misleading-graphics gallery

The misleading-graphics gallery needs JavaScript.

AI-output audit

This AI audit needs JavaScript.

Hands-on visual lab

Twelve campuses that share one true pass rate. The league table still has a winner and a loser, and both are decided by the denominator. Then run the browser-R lab below without leaving QuantegyAI.

This visual lab needs JavaScript.

GeoGebra lab

Use GeoGebra sliders as a dashboard-prototyping layer: one control, one decision rule, one verified metric.

Interactive Math with AI / GeoGebra strand

Embedded GeoGebra applet loading…

R lab

R / Quarto / Shiny workflow

Build a Shiny mini-dashboard with reactive filters, denominator notes, and linked interpretation.

# 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 corrections

Adaptive reflection

What I know / where I go next

I can state the numerator, denominator, window, and exclusions behind a metric. I can recognise when an aggregate reverses the pattern in every subgroup. I can set a minimum cell size and explain why a percentage can disclose a person. I can tell a wrong mapping from a misleading encoding from an unsupported claim. I can name who is missing from my capstone's data and what that omits.

Mastery check loading…

Capstone connection Add this module’s evidence to your capstone readiness tracker. If your explanation depends on AI, include the prompt, output, verification method, and final correction.