Science Unit 4, Data Science 2 ~25 min Checkpoint 2

Checkpoint 2

Review the key ideas from Lessons 6-10, then test yourself with 10 multiple-choice questions and 3 short-answer questions.

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1

Science vs Pseudoscience

Focus: A claim is scientific when it can be tested and could, in principle, be shown to be false. Real science rests on measured evidence over anecdote, on peer review and reproducibility, and on being self-correcting when better evidence arrives. Pseudoscience imitates science but cannot be falsified and refuses to change.

Key terms: Testable, Falsifiable, Self-correcting

2

Pseudoscientific Claims in Popular Media

Focus: Pseudoscience thrives in ads, influencer posts and clickbait, where the goal is to persuade and sell rather than to prove. Watch for red flags such as testimonials instead of trials, "clinically proven" with no named study, miracle results, and fear words like "toxins". Fact-check by tracing a claim to its original source.

Key terms: Testimonial, Red flag, Fact-checking

3

Distorting Data to Mislead

Focus: Accurate numbers can still be drawn to mislead. Common tricks include a truncated y-axis that exaggerates small differences, cherry-picking a window of data, misleading averages dragged by an outlier, and percentages with no base. Defend yourself by checking the axes, the sample, the source and what has been left out.

Key terms: Truncated axis, Cherry-picking, Misleading average

4

Is It Pseudoscientific? Evaluating Claims and Theories

Focus: You can judge any claim with a framework of questions: Is it falsifiable? Is there peer-reviewed evidence? Has it been replicated? Does it cherry-pick? Is the mechanism plausible? Does it shift the goalposts or self-correct? A claim being new or unpopular does not make it pseudoscience, dodging the tests does.

Key terms: Framework, Falsifiability, Replication

5

Large Datasets: Features, Collection and Uses

Focus: A large dataset is described by volume (number of records), variety (different things measured) and velocity (speed new data arrives). Each row is a record and each column is a variable, with metadata describing units and how the data was gathered. Large datasets reveal patterns but can still be biased by how they are collected.

Key terms: Volume, Variety, Velocity

1. Which feature is the most important sign that a claim is scientific?

AIt uses impressive, technical-sounding words
BIt is believed by a large number of people
CIt is testable and could, in principle, be shown to be wrong
DIt has been around for a very long time

2. An old scientific idea is later proven wrong by better evidence and is replaced. This shows that science is:

ASelf-correcting, which is a strength
BPseudoscientific, because it was wrong
CUntrustworthy and should be ignored
DNo better than astrology

3. An advert says a product is "clinically proven" but names no study you can read. This is best described as:

AStrong scientific evidence
BA red flag, because no real evidence is offered to check
CA peer-reviewed result
DProof the product is safe

4. What is the single most useful first step when fact-checking a dramatic health claim in an ad?

ACount how many likes the post has
BRead the before-and-after photos closely
CCheck whether the wording sounds scientific
DTry to find the original study and check who funded it

5. A bar chart uses correct numbers but starts its vertical axis at 95 instead of 0, making a small difference look huge. This trick is called:

AA truncated y-axis
BA controlled trial
CPeer review
DInterpolation

6. Nine workers earn $50,000 each and the boss earns $950,000. Which statement is true about describing a typical wage?

AThe mean of $50,000 describes a typical worker well
BThe mode is the fairest summary here
CThe median of $50,000 describes a typical worker better than the mean
DThe boss's wage has no effect on the mean

7. A seller says: "If the crystal does not work, it means your negative energy was too strong." This reveals that the claim is:

AFalsifiable and well supported
BNot falsifiable, because no result could ever count against it
CBacked by peer-reviewed evidence
DAn example of good self-correction

8. A new cancer drug is in clinical trials and has not yet been proven to work. Using the claim-check framework, this is best described as:

APseudoscience, because it is unproven
BPseudoscience, because it is new
CProven science, because it is in a trial
DReal science that is testable but not yet supported by evidence

9. In a weather dataset, "variety" describes:

AThe sheer number of records stored
BThe number of different things measured, such as temperature, rainfall and wind
CHow quickly new readings arrive
DWhether the data is stored on a computer

10. A fitness app collects step counts only from people who own an expensive smartwatch, then claims to describe how active all Australians are. The main problem is:

AThe data is biased, because it only captures one type of person
BThe dataset is too small to be useful
CStep counts cannot be measured at all
DA large dataset is always perfectly fair
SA1

Explain two features that make a claim scientific, and describe one warning sign that a claim is pseudoscientific. (4 marks)

Write your answer in your book.
SA2

A graph uses accurate numbers but still misleads the reader. Describe two ways data can be distorted, and explain how you would check each one. (4 marks)

Write your answer in your book.
SA3

Describe the three features of a large dataset (volume, variety and velocity), and explain why a very large dataset can still give misleading results. (5 marks)

Write your answer in your book.
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Test Your Knowledge

Put what you have reviewed to the test! Jump through the checkpoint questions in game form.

Play Game

Mark this checkpoint as complete

Tick the box when you have finished the questions and played the game.

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