Two ways accurate data can still mislead in a chart

What's really being asked

This checks whether you understand that a chart communicates through human perception, so accurate numbers can still produce a false impression. It separates people who can read data from people who can present it responsibly.

The full answer

First distortion: a truncated y-axis. Starting a bar chart's axis at, say, ninety instead of zero makes a one-point difference look like a doubling because the eye compares bar lengths. The fix is to anchor bar charts at zero; if you must zoom, use a line chart or clearly annotate the broken axis. Second distortion: a cherry-picked time window or a dual-axis chart. Showing only the months where two series move together implies a trend or correlation that vanishes over the full range, and dual axes let you slide two unrelated lines into apparent lockstep by choosing the scales. The fix is to show the complete relevant time range and avoid dual axes, or use indexed values on a single shared scale. You can also mention area encodings where doubling a value but scaling both width and height quadruples the perceived size.

The mistakes people make

Listing chart types like pie charts as simply bad without explaining the perceptual mechanism. Saying the data is wrong, which misses the premise that the numbers are accurate. Offering no concrete correction.

What usually comes next

When is a non-zero axis actually acceptable? How would you detect a misleading chart in someone else's report? What is the lie factor concept?

A concrete example

A sales deck shows quarterly revenue as bars from ninety-eight to one hundred, making Q4 look twice Q1. Rescaled from zero, the bars are nearly identical, revealing flat sales. You redraw it anchored at zero and add a small annotation noting growth was two percent, turning a hype slide into an honest one.