Lecture 28 — Summaries That Don't Mislead

Position in the course

This is lecture 28 of 32. It belongs to the course arc described in the course outline and uses the standard interactive case method: display, attempt (5 minutes), discuss, reveal, name the technique.

Learning objectives

By the end of this lecture students can do all three under time pressure, which is what the four cases exercise.

Prerequisite knowledge

Before this lecture: Lecture 25 — thinking with data

Assumed fluencies:

Preparation task: Bring one published statistic and guess its base.

Detailed topic outline

Core (mandatory):

  1. mean vs median under skew
  2. percentage bases and their traps
  3. the inspection paradox
  4. Simpson's paradox informally

Extension (optional enrichment):

  1. dashboard ethics
  2. defending a summary to a sceptical room

Case sequence

CaseTitleDifficulty
cs-109The Salary ParableBeginner
cs-110The Percentage MazeFoundational
cs-111The Waiting-Time TwistIntermediate
cs-112The Dashboard DefenceAdvanced
cs-135The Split That LeaksExpert

Display each case full-screen from the student page (cases cs-109–cs-112); instructor pages cs-109–cs-112 under case-studies/instructor/ hold the full solutions, reveal guides, and discussion prompts. Applied additions cs-135 extend the session as optional extra attempts or homework; they share the lecture's theme but teach an applied, real-world angle.

Timing plan (120 minutes)

MinutesSegment
0–10Framing: where this fits; recall of the previous lecture
10–30Case 1 (cs-109): 5-minute attempt, then discussion and reveal
30–50Case 2 (cs-110): same rhythm
50–70Case 3 (cs-111): same rhythm, harder case
70–75Break
75–100Case 4 (cs-112): same rhythm, hardest case
100–115Synthesis: name the techniques; misconceptions check
115–120Exit questions, homework, next-lecture preview

Timing flexes with the room: if a case ignites the class, let the discussion run and compress the synthesis; if the attempts stall, harvest partial attempts rather than extending the clock.

Worked examples

Instructor demonstration to open the lecture: Salary parable (cs-109): add one billionaire to a row of nine salaries and watch the mean betray; the median holds.

Classroom activities

#ActivityMinutesFormat and prompt
1Mean-vs-median showdown6Ten salary strips; instant votes, then the skew that decides them.
2Percentage court7Three published claims are tried: is the base stated, and is the comparison legal?
3Dashboard redesign6A misleading chart is redesigned under the constraint 'honest but still persuasive'.

Board plan

Instructor explanation notes

Reading the room. Summaries are arguments, and this lecture teaches students to cross-examine them. The inspection paradox (cs-111) is the intellectual peak; simulation makes it believable.

Running the reveal. Ethics is treated as design under constraint (cs-112), not as a bolt-on sermon.

Lecture focus. One sentence to repeat verbatim: Every summary is an argument; cross-examine it.

Common misconceptions

Each case's instructor page lists the specific wrong turn to expect; the reveal should address it by name.

Student practice exercises

Core:

  1. Recompute the salary parable's (cs-109) mean and median with and without the outlier.
  2. Untangle the percentage maze (cs-110): identify each base and restate the claims correctly.
  3. Explain the waiting-time twist (cs-111) in four sentences to a commuter.

Enrichment (optional):

Formative assessment

Exit questions (choose one; answer on a card in 60 seconds):

  1. When must the median replace the mean? Expected: under skew or outliers — any time a tail can dominate
  1. What is the percentage maze's core error? Expected: comparing percentages with different bases
  1. State the inspection paradox in one line. Expected: sampled experiences oversample long waits/stays — the sampling is length-biased

Cards are skimmed before the next lecture; three answers become the framing. Persistent wrong answers route students to the review stations of lecture 16 or to office hours.

Homework and preparation

Learning-outcome mapping

Differentiation