Year 9 Science · Unit 4 · Lesson 10

Large Datasets: Features, Collection and Uses

Challenge Worksheet

Name

Date

Class

Learning Goals

Critically evaluate the strengths and limits of a large dataset.
Justify why size alone does not make a dataset trustworthy, using quality, bias and privacy.
Design how a small set of readings could be grown into a genuine large dataset.

Warm Up

Odd one out

Circle the item that does not belong in each group. Then explain why it doesn't fit in the answer column.

#	Group	Your answer (odd one + reason)
1	Volume Variety Velocity Victory
2	Table of temperatures Spreadsheet of rainfall Satellite photo List of heights
3	Automated sensors Satellites National census A single guess
4	Data quality Bias in collection Privacy The colour of the table

Go Deeper

Evaluate the dataset

A company claims...

"Our app holds 50 million records, so it is the most trustworthy picture of Australian eating habits ever made. We collected it from people who chose to log their meals in our paid premium app, and we never tell users what units the kilojoule figures are in or which year each record is from."

(a) Explain why having 50 million records does not, by itself, make this dataset trustworthy. Refer to at least two of: data quality, bias in collection, and metadata.

Challenge 3 marks

(b) The data only comes from people who chose to use a paid premium app. Explain how this could bias the conclusions the company draws about all Australians.

Challenge 3 marks

(c) A class records the outdoor temperature once a day for two weeks. Design a plan to turn this small set of readings into a genuine large dataset. State one change for each of volume, variety and velocity, and explain why your final dataset would count as large.

Challenge 4 marks

Wrap Up

In one sentence, what was the main idea of this lesson?