Clarity of message in a visualisation

Name:

Date:

Class:

Quick Questions

Shade in the A or B circle for each question.

1. Which chart title gives the clearest message?

A Sales data

B Sales rose 25% after the March campaign

2. A column chart has 3D effects, a patterned background and heavy gridlines. What should you do to improve clarity of message?

A Remove the clutter so the data stands out

B Add more decoration to hold attention

3. True or false: a single visual should try to show five different findings so the viewer gets everything at once.

A False

B True

4. Which axis label is more readable?

A Average monthly rainfall (mm)

B avg_mthly_rnfl_val

5. A viewer takes two minutes to work out what a chart is saying. What does this show?

A The message is clear

B The message is not clear

Match the Pairs

Draw a line connecting each item on the left to its match on the right.

Left column		Right column
1title: "Sales rose 20% in Term 3"		AToo many messages are crammed into one visual, which hides the main point.
2gridlines: none, 3D effect: off		Bchart1 = "Sales by month" chart2 = "Returns by region"
3One message per visual: each chart makes a single point		CRemoving clutter so nothing distracts the viewer from the main point
4Readable labels: text is large enough and clearly worded		DThe title describes nothing and does not state the finding, so the viewer cannot tell the main point.
5title: "Chart 1"		Eaxis_label_size = 12 axis_title = "Sales ($)"
6chart = sales + returns + staff + costs		FA title that states the finding, so the viewer gets the main point straight away

Definitions

Clarity of message

Fill in the Blanks

Complete each line of code by filling in the missing parts.

plt._____("Sales rose 20% in March")

plt.______("Month")

plt.xticks(________=45)

plt.grid(_____)

plt.title("Sales rose 20% in March") plt.______() plt.title("Costs fell in June")

Spot the Error

Shade in the letter of the line that contains an error.

Question 1

A plt.bar(months, sales)

B plt.xlabel("Month")

C plt.ylabel("Sales ($)")

D plt.title("Sales data")

E plt.show()

Question 2

A plt.plot(days, temp)

B plt.title("Friday is hottest")

C plt.grid(True, lw=3, c="red")

D plt.show()

Question 3

A bars = plt.bar(teams, wins)

B plt.title("Red team won most")

C plt.xlabel("x")

D plt.ylabel("Wins")

Question 4

A plt.bar(subjects, marks)

B plt.xticks(color="white")

C plt.title("Maths scored best")

D plt.show()

Question 5

A fig, ax = plt.subplots()

B ax.bar(brands, share)

C ax.set_title("Chrome leads")

D ax.set_facecolor("hotpink")

E plt.show()

Answer Key

Clarity of message in a visualisation

Section A: Quick Questions

B — Sales rose 25% after the March campaign

A — Remove the clutter so the data stands out

A — False

A — Average monthly rainfall (mm)

B — The message is not clear

Section B: Match the Pairs

title: "Sales rose 20% in Term 3" → A title that states the finding, so the viewer gets the main point straight away

gridlines: none, 3D effect: off → Removing clutter so nothing distracts the viewer from the main point

One message per visual: each chart makes a single point → chart1 = "Sales by month" chart2 = "Returns by region"

Readable labels: text is large enough and clearly worded → axis_label_size = 12 axis_title = "Sales ($)"

title: "Chart 1" → The title describes nothing and does not state the finding, so the viewer cannot tell the main point.

chart = sales + returns + staff + costs → Too many messages are crammed into one visual, which hides the main point.

Section C: Definitions

Clarity of message — When a viewer can state a visualisation's main point in seconds, thanks to a title that states the finding, no clutter, one message per visual and readable labels.

Section D: Fill in the Blanks

plt.title("Sales rose 20% in March")

plt.xlabel("Month")

plt.xticks(rotation=45)

plt.grid(False)

plt.title("Sales rose 20% in March")
plt.figure()
plt.title("Costs fell in June")

Section E: Spot the Error

D — The title only names the topic, so the viewer has to work out the point themselves. A clear title states the finding, for example "Sales up 20%".

C — Thick red gridlines are clutter that pulls attention away from the line. Clutter should be removed, so the grid should be switched off with plt.grid(False).

C — The label "x" tells the viewer nothing. Labels must be readable and descriptive, so it should say what the axis shows, such as plt.xlabel("Team").

B — White tick labels on a white background cannot be read. Labels need strong contrast, so the colour should be "black".

D — A hot pink background is a distracting clutter that competes with the bars. Keep the background plain, so it should be "white".