What Counts as Scientific Data?
In 2023, the Bureau of Meteorology recorded Sydney's hottest June day at 25.9°C — but only because every station uses calibrated instruments, not feelings.
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Your friend says it is 30 degrees outside because a weather app says so. Another friend says it feels hotter because they are sweating.
Which is scientific data and which is opinion? How do you know?
Two students stand outside at 9 am. One says "it feels pretty cold today." The other checks a thermometer and writes "13.2°C" in her notebook. Next week, a third student reads the notebook and knows exactly what the temperature was — but cannot do anything useful with "felt pretty cold." Scientific data is information gathered through systematic observation or measurement. It must be observable (anyone can detect it), recordable (written down with units and context), and repeatable (another person doing the same measurement should get a similar result). Without these three qualities, information is just an opinion or an anecdote.
When scientists collect data, they start with a clear question. They choose tools that give reliable readings — thermometers, rulers, timers, pH probes — and they record exactly what they see, not what they expect to see. This discipline is what separates science from guesswork.
Saying 'the plant grew a lot' is vague and unscientific. Saying 'the plant grew 5 cm in two weeks' is scientific data because it is specific, uses a standard unit, and could be checked by another observer.
The Bureau of Meteorology operates over 700 weather stations across Australia. Every temperature reading is taken with calibrated instruments, recorded digitally, and checked against nearby stations. If a reading feels wrong to a meteorologist, the instrument data still stands — feelings do not override measurements.
Many students think that any story or personal experience counts as data. It does not. Your uncle's claim that 'eating carrots cures colds' is an anecdote, not data. Data requires systematic collection, accurate recording, and the possibility of verification by others.
Know
- Scientific data is information collected through observation or measurement.
- Data must be recorded accurately to be useful.
Understand
- Not all information counts as scientific data; it must be objective and repeatable.
- Data forms the foundation of scientific conclusions.
Can Do
- Distinguish between data and opinion in everyday situations.
- Explain why accurate recording matters in science.
Wrong: Any number you write down is scientific data.
Right: Scientific data must be collected systematically, recorded accurately, and be relevant to the question being investigated.
Wrong: Data and evidence are the same thing.
Right: Data becomes evidence only when it is used to support or challenge a specific claim or explanation.
Wrong: All numbers are automatically scientific data.
Right: A number only becomes scientific data when it is collected systematically, recorded properly and is relevant to a scientific question.
Wrong: Observations and inferences are the same thing.
Right: An observation is what you detect with your senses or instruments. An inference is what you think it means. Keep them separate when recording data.
A student writes about an experiment. One line has an error — click it.
- I measured the water temperature with a thermometer three times.
- The water felt too hot to me, so the thermometer must be wrong.
- I recorded all my results in a table with units and headings.
Objective information is based on facts that anyone can measure or observe. Subjective information depends on personal feelings, opinions or interpretations. Scientific data must always be objective because science relies on agreement between different observers.
To make information objective, you need three things: a clear measurement method, a standard unit, and a recording system that captures exactly what was measured. 'The water is 12 degrees Celsius' is objective because any person with a thermometer can verify it. 'The water is cold' is subjective because one person's 'cold' is another person's 'refreshing'.
A student reports that 'the reaction mixture turned dark blue after 45 seconds.' This statement is objective because the colour and time can be verified by another observer. If they had written 'the reaction looked scary,' that would be subjective and unscientific.
At ANSTO, Australia's nuclear science facility, technicians measure radiation levels with precision instruments. A technician might feel perfectly safe in an area, but if the Geiger counter reads above the safety threshold, the objective data determines the procedure — not personal comfort.
Some students believe that writing down any number makes it scientific data. It does not. A number only becomes data when it is collected systematically, recorded with units and context, and relevant to the question being investigated. A random number scribbled on a napkin is not data.
Scientific data comes in two main flavours. Quantitative data uses numbers — counts, measurements, or calculations. Mass, time, temperature, and volume are all quantitative. Qualitative data uses words to describe qualities: colour, texture, smell, sound, or behaviour.
Neither type is automatically better. A biologist studying a rare frog might record 'calls at dusk, brown with green spots' alongside 'body mass 12 grams, call frequency 2.4 kHz.' The qualitative description helps identify the species; the quantitative measurement lets them track changes in population health over time.
A chemist observes a reaction and records: 'bubbles formed rapidly, the solution turned milky white, and the temperature rose from 22 degrees Celsius to 31 degrees Celsius in ten seconds.' The colour and bubbling are qualitative; the temperature readings are quantitative. Together they give a complete picture.
CSIRO ecologists monitoring the Great Barrier Reef record both qualitative descriptions — 'coral appears bleached, algae overgrowth visible' — and quantitative measurements such as seawater pH and temperature. Either type alone would miss part of the story.
Some students think quantitative data is always 'better' because it uses numbers, while qualitative data is just 'vibes.' This is wrong. Good science uses both. A number without context is meaningless, and a detailed description without measurement cannot show change over time.
If data is recorded incorrectly, every conclusion drawn from it will be flawed. Scientists take extraordinary care when measuring and recording for this reason. A single misplaced decimal, a misread scale, or a forgotten unit can cascade through an entire study and produce results that are completely wrong.
Repeatability is the safeguard. When multiple scientists can repeat your measurement and get the same result, confidence in the data grows. This is why published research includes detailed methods sections — so others can replicate the work and check for errors.
A student misreads a measuring cylinder and records 25 mL instead of 2.5 mL. They then calculate a concentration using this wrong volume. Every subsequent graph, table, and conclusion is built on a tenfold error that began with one careless reading.
The Australian Synchrotron near Melbourne produces measurements accurate to fractions of a nanometre. Researchers there check and recalibrate instruments daily because even tiny errors in molecular measurements would make drug designs or material studies worthless.
Some students think small mistakes do not matter because they are only 'a little bit wrong.' In science, small errors compound. A temperature misread by 2 degrees might seem minor, but if it affects a reaction rate calculation, the final conclusion could be entirely backwards.
A researcher enters a koala's mass as 1.25 kg instead of 12.5 kg in a database. By the time this error is multiplied across a study of 500 koalas, how much total mass is missing from the population estimate?
How close was your prediction?
Nice calibration — your intuition is good for this kind of problem.
Good — being surprised is the point. This answer is worth remembering.
Speed Round · 6 questions
True or false? Tap as fast as you can. Build a streak.
Scientific data can be collected through observation or measurement.
Data and evidence are exactly the same thing.
Objective information depends on personal feelings.
"The water is 12 degrees Celsius" is an example of objective data.
Qualitative data always involves numbers.
Repeatability means others can obtain the same result when an experiment is repeated.
How are you completing this lesson?
Think back to the opening scenario about the weather app and how hot it feels.
How would you turn the feeling of heat into objective scientific data that everyone could agree on?
Quick Check · 5 questions
Check Your Understanding · 3 questions
1. Give one example of objective data and one example of subjective opinion about the weather today.
2. Why is repeatability important when collecting scientific data?
3. Explain the difference between data and evidence in your own words.
Show Your Working · 3 questions
SA1. Describe two differences between objective and subjective information, and give an example of each.
SA2. Explain why accurate data collection is essential for drawing reliable scientific conclusions.
Hint: Consider what happens if measurements are recorded incorrectly.
SA3. A student claims that 'all numbers are scientific data'. Evaluate this statement using examples.
Quick Check
1. B — 'The flower is red and 8 cm wide' is objective and measurable.
2. C — Objective data can be measured and agreed upon by anyone.
3. B — Repeatability allows others to verify results and build confidence.
4. B — Data becomes evidence when used to support a claim.
5. B — 'Very hot' is subjective and lacks a numerical measurement.
Show Your Working Model Answers
SA1 (4 marks): Objective information is based on facts and can be measured by anyone [1], e.g. 'the temperature is 25°C' [1]. Subjective information depends on personal feelings [1], e.g. 'the weather is nice' [1].
SA2 (4 marks): If data is recorded incorrectly, every conclusion drawn from it will be flawed [1]. Small errors can lead to big mistakes in understanding [1]. Scientists double-check measurements to ensure reliability [1]. Accurate data allows others to repeat and verify the investigation [1].
SA3 (5 marks): This statement is incorrect [1]. A number only becomes scientific data when collected systematically, recorded properly and relevant to a question [1]. For example, '7' written randomly is not scientific data [1], but 'the plant grew 7 cm in one week' is scientific data because it is measurable, objective and relevant [1]. Scientific data must also be repeatable by others [1].
Data
Information from observation or measurement
Objective
Measurable by anyone, no personal bias
Evidence
Data used to support a claim
Quantitative
Numerical data (mass, time, temp)
Qualitative
Descriptive data (colour, texture)
Repeatability
Same result when repeated
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