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Data Storytelling

Total questions: 20

Worksheet time: 10mins

Name
Class
Date
1.

What is data storytelling?

a)

The use of statistics to test hypotheses.

b)

The process of collecting large datasets.

c)

The process of cleaning data.

d)

The combination of data, visuals, and narrative to communicate insights.

2.

Which of the following is NOT a core element of data storytelling?

a)

Programming

b)

Visualisation

c)

Narrative

d)

Data

3.

A data story should always include all available data.

a)

True

b)

Maybe

c)

False

4.

Which visual is most appropriate to show trends over time?

a)

b)

c)

d)

5.

Why is the audience important in data storytelling?

a)

They choose the data source.

b)

They determine the size of the dataset.

c)

They influence the design, language, and focus of the story.

d)

They decide the statistical method used.

6.

Good data storytelling can influence decision-making.

a)

True

b)

Maybe

c)

False

7.

Which practices improve clarity in a data story?

a)

Removing unnecessary visuals

b)

Using consistent labels and scales

c)

Adding as much data as possible

d)

Highlighting key insights

8.

Which methods can be used to handle missing data?

a)

Deleting rows with missing values

b)

Imputing values (mean, median, mode, etc.)

c)

Ignoring missing values completely

9.

A dashboard with too many visuals mainly causes:

a)

Cognitive overload

b)

Higher engagement

c)

Better insight

d)

Faster decisions

10.

Which of the following is an example of missing data?

a)

Age = 25

b)

Income = NULL

c)

Gender = Male

d)

Country = Malaysia

11.

Which component turns data into a meaningful message?

a)

Narrative

b)

Database

c)

Storage

d)

Algorithm

12.

What are the suitable data visualization tools?

a)

b)

c)

d)

13.

When should data cleaning be performed?

a)

Only during data collection

b)

Only after analysis

c)

Before and during analysis

d)

Only when errors are found

14.

Which chart is most suitable for comparing categories?

a)

b)

c)

d)

15.

When communicating data, what should you consider first?

a)

The size of the dataset

b)

The audience and their needs

c)

The software you will use

d)

The colour of the charts

16.

Which practice is unethical in data visualisation?

a)

Manipulating axes to exaggerate trends

b)

Using clear labels

c)

Citing data sources

d)

Showing uncertainty

17.

What is the role of narrative in data journalism?

a)

To simplify data by removing detail

b)

To replace evidence

c)

To focus on emotions only

d)

To organise and explain findings in a meaningful way

18.

Which tool is commonly used to create interactive dashboards?

a)

b)

c)

d)

19.

What should a data journalist do before publishing a data-driven story?

a)

Hide the data sources

b)

Verify the data and check for errors

c)

Add more visuals

d)

Remove uncertainty

20.

Graphical integrity is mainly about:

a)

Speed

b)

Style

c)

Honesty

d)

Interactivity