21 What aspect of data visualisation bias did Amara find most surprising?
A How often it happens intentionally
B The impact of colour choices
C How software defaults cause it
22 Where did Felix find the example of a truncated graph?
A A national newspaper
B A corporate report
C A university website
23 What do the students decide to include in their presentation introduction?
A A dictionary definition
B A real-world scenario
C A longer opening statement
24 Which demographic group was misrepresented in their main case study?
A Teenagers
B Pensioners
C Toddlers
25 What did their tutor, Dr Hemlock, advise them to pay more attention to?
A The source of the data
B The audience’s background
C The axes labels
Questions 26 to 30
What solution do the students agree on for each of the following types of bias?
Choose FIVE answers from the box and write the correct letter, A-G, next to Questions 26-30.
Solutions
A Provide the full timeline
B Stick to flat designs
C Start at zero
D Use direct annotations
E Rely on neutral tones
F Add a secondary chart
G Group minor categories
Types of Bias
26 Cherry-picking data
27 Inappropriate scaling
28 Misleading colours
29 3D chart distortion
30 Overcomplicated legends
Keys
21 C
22 A
23 B
24 B
25 C
26 A
27 C
28 E
29 B
30 D
Transcripts
Part 3: You will hear two students discussing their presentation on data visualisation bias.
AMARA: Hi Felix. Ready to work on our presentation about data visualisation bias?
FELIX: Yes, let’s get started. I’ve been doing some reading this morning.
AMARA: Me too. Before researching, I honestly thought most misleading graphs were created on purpose to trick people. But what actually shocked me was how often it happens just because of the software defaults.
FELIX: Exactly. The automatic settings cause it frequently. People just click a button and don’t realise the program is stretching visual elements.
AMARA: Anyway, about specific examples for the class. Did you find one illustrating a truncated graph? I looked at that corporate report, but it was a bit boring.
FELIX: No, that wasn’t clear. I actually grabbed a really obvious one from a national newspaper. It cuts off the bottom half of the bar chart completely.
AMARA: Perfect, a national newspaper is very relatable. Now, what about our introduction? Should we make it longer?
FELIX: Not longer, no. And I don’t think just reading a dictionary definition of bias is good enough either. We really need an everyday situation to hook the audience.
AMARA: Good point, let’s put in a real-world scenario. That will make the concept easier to understand.
FELIX: Right. Moving on to our main case study, the pie chart we chose totally misrepresents the older generation.
AMARA: You mean the pensioners?
FELIX: Yes. The teenagers and toddlers were grouped correctly, but the section representing pensioners was completely squeezed out of proportion due to the data split.
AMARA: Okay, I’ll highlight that. Oh, remember what our tutor, Dr Hemlock, advised us to do? I thought he said we need to look closely at the source of the data.
FELIX: Actually, he was happy with our sources. He specifically told us to double-check the axes labels. That is where creators often manipulate the scale.
AMARA: Right, the labels. I’ll make sure we point those out.
FELIX: So, let’s decide on practical solutions for each bias. First up is cherry-picking data.
AMARA: That is when people only show a few months of positive results. It happens constantly in sales.
FELIX: Exactly. To fix that, we must always provide the full timeline in our charts. No skipping months.
AMARA: Agreed. Next is inappropriate scaling.
FELIX: The fundamental rule for bar charts is simple. You can’t begin the y-axis anywhere. It should always start at zero.
AMARA: Yes, otherwise differences look exaggerated. What about misleading colours?
FELIX: Some people use bright neon shades to make certain data look alarming.
AMARA: We should advise the class to rely on neutral tones instead. It keeps things objective.
FELIX: Neutral tones it is. Now, what about 3D chart distortion?
AMARA: They completely mess up the proportions. The front always looks massive.
FELIX: The only real fix is to stick to flat designs. Two-dimensional layouts are more reliable.
AMARA: Yep, flat designs. Finally, overcomplicated legends. Sometimes there are eight different trend lines, and you have to look back and forth to the box.
FELIX: That is very annoying. Some suggest grouping minor categories together to reduce clutter.
AMARA: Actually, I think it is better to just put the text right next to the lines.
FELIX: Oh, you mean use direct annotations? Yes, I agree, that is much clearer.
AMARA: Great, that is all our solutions sorted. Let’s start drafting the slides.
