← VCE Data Analysis

Data quality: reliability

Data is reliable when it comes from a trustworthy source and gives consistent results if collected again in the same way. A larger sample size also makes results more reliable.

Part of: VCE Applied Computing - U1O1 - Data analysis — Module 32, ACARA Digital Technologies Years 9–10 — Acquiring and Validating Data — Module 88

Understanding Data quality: reliability in VCE Data Analysis

What it does: A well-known, trusted organisation is a credible source, so its data can be relied on more than data from an unknown website. When the same survey is repeated and gives very similar results (72 and 71), the data is consistent and so reliable. Source credibility matters. A government statistics agency uses documented methods, while an anonymous post cannot be checked. A larger sample size makes results more reliable because one or two unusual answers have less effect on the overall result.

Common mistakes: Reliable data needs a trustworthy source AND a large enough sample. OR would pass data with only one of these. It should be IF trusted AND size > 100. To check whether repeated collection gives consistent results, you find the difference between the two results, not the sum. It should be diff ← ABS(result1 - result2).

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This module's key terms are also in a printable crossword (Modules 25–37) and word search (Modules 25–54).

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Learning
Duo
Which data is more reliable?
Student attendance figures from the school's official recordsAttendance figures a student guessed from memory
Matches
source = "anonymous blog post" # reliable
↓
An anonymous blog is not a credible source, so the data should not be called reliable without checking it against a trusted source.
Applying
Meanings
Term
reliability
Meaning
Data is reliable when it comes from a trustworthy source and gives consistent results if collected again in the same way. A larger sample size also makes results more reliable.
Practice
=COUNT(B2:B101)
Testing
Gaps
Data is if results are consistent
Check Answer
Spots
3size ← 500
4IF trusted OR size > 100

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Quick Questions

1. Which data is more reliable?
A. Student attendance figures from the school's official records ✓
B. Attendance figures a student guessed from memory
Reliable data comes from a trustworthy source. Official records are checked and maintained, while guesses from memory are not.
2. True or false: Reliable data gives consistent results when it is collected again in the same way.
A. False
B. True ✓
Repeatable collection is a key sign of reliability. If the same method gives very different results each time, the data is not reliable.
3. A class measures the temperature of the same room at the same time using the same thermometer three days in a row. The readings are 22°C, 22°C and 23°C. What does this suggest?
A. The data is reliable because the results are consistent ✓
B. The data is unreliable because the readings are not all identical
Small differences are normal. Results that are very close when collected the same way show the data is consistent and reliable.
4. Which source is more credible for data about Australian population numbers?
A. An anonymous social media post
B. The Australian Bureau of Statistics ✓
Source credibility matters. A government statistics agency uses documented methods, while an anonymous post cannot be checked.
5. Which survey is likely to give more reliable results?
A. A survey of 500 randomly chosen students ✓
B. A survey of 5 friends
A larger sample size makes results more reliable because one or two unusual answers have less effect on the overall result.

Match the Pairs

reliability → Data is reliable when it comes from a trustworthy source and gives consistent results if collected again in the same way
sample_size = 1000 → A larger sample gives more reliable results than a very small sample because one-off or odd answers have less effect
A source is credible, so data from it is more reliable → source = "Australian Bureau of Statistics"
Repeating the same collection method should give consistent results, which shows reliability → survey1 = 72 and survey2 = 71
source = "anonymous blog post" # reliable → An anonymous blog is not a credible source, so the data should not be called reliable without checking it against a trusted source.
sample_size = 3 # very reliable → A sample of only 3 is too small to be very reliable because results could change a lot if it were collected again.

Key Term

reliability
Data is reliable when it comes from a trustworthy source and gives consistent results if collected again in the same way. A larger sample size also makes results more reliable.

Fill in the Blanks

1. Data is reliable if results are consistent
Hint: Type the data quality term this module is about
2. Reliable data comes from a trustworthy source
Hint: Type the word describing a source you can believe and depend on
3. Collect it again and get consistent results
Hint: Type the word meaning the results match each time
4. A larger sample size makes data more reliable
Hint: Type the word for the group of people or items data is collected from
5. Repeatable: use the same method each time
Hint: Type the word for the way the data is collected, which must stay the same

Spot the Error

Question 1
A source ← "ABS census"
B trusted ← TRUE
C size ← 500
D IF trusted OR size > 100
E OUTPUT "Reliable"
Line D — Reliable data needs a trustworthy source AND a large enough sample. OR would pass data with only one of these. It should be IF trusted AND size > 100.
Question 2
A result1 ← 72
B result2 ← 71
C diff ← result1 + result2
D IF diff <= 2 THEN
E OUTPUT "Consistent"
Line C — To check whether repeated collection gives consistent results, you find the difference between the two results, not the sum. It should be diff ← ABS(result1 - result2).
Question 3
A readings ← [48, 51, 50, 49]
B total ← SUM(readings)
C count ← LENGTH(readings)
D average ← total * count
E OUTPUT average
Line D — The average of repeated readings is the total divided by the number of readings, not multiplied. It should be average ← total / count.
Question 4
A sample_size ← 20
B IF sample_size > 30 THEN
C OUTPUT "Collect more data"
D ENDIF
Line B — A small sample is less reliable, so the warning should show when the sample size is LESS than 30. It should be IF sample_size < 30 THEN.
Question 5
A source ← "random blog"
B credible ← FALSE
C IF credible = TRUE THEN
D OUTPUT "Check source"
E ENDIF
Line C — The warning to check the source should appear when the source is NOT credible. It should be IF credible = FALSE THEN.

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