ACARA v9 CONTENT DESCRIPTION “analyse methods, conclusions and claims for assumptions, possible sources of error, conflicting evidence and unanswered questions”
Builds on planning fair tests and recording results. The skill now is to look back at an investigation with a critical eye: which statements are genuine evidence, and which are really assumptions, mistakes in the method, or claims that go further than the data allows.
Not every statement in a report is evidence
When you read a conclusion, it pays to separate what was actually measured from what was assumed or overstated. Genuine evidence comes straight from the observations. Assumptions are things taken for granted, sources of error are flaws in how the data was gathered, and some claims simply reach beyond what a single experiment can show. Sorting these apart is how scientists decide whether a conclusion can be trusted.
Which statements really back the claim?
A group placed identical bean seedlings in red, green and blue light and reported that plants grow tallest in red light. Decide whether each statement is sound evidence for that claim, or an assumption or flaw that weakens it.
Claim: Bean seedlings grow tallest under red light.
After two weeks the seedlings in red light were on average taller than those in green or blue light.
Each colour group had the same number of seedlings, the same soil and the same amount of water.
The red lamp was much brighter than the others, so the red plants also received more total light.
Red is a warm colour, so it must give plants more energy than cool colours.
Only one seedling was grown in each colour, and it was not measured again.
Decide whether each statement is evidence for the claim, or not.
Assumptions, errors and gaps weaken a case
A claim can sound convincing yet rest on shaky ground. If the lamps differed in brightness, colour was not the only thing that changed, which is a flaw in the method. Reasoning that red feels warm so it must help is an assumption, not a measurement. And testing just one plant per colour leaves an unanswered question about whether the pattern would hold. Naming these weaknesses is the heart of analysing a conclusion.
A source of error often hides in the raw data
Some flaws never appear in the conclusion at all; they sit in the measurements themselves. A single reading that does not fit the others can be the fingerprint of a slip in the method, a misread scale or a moment the equipment was disturbed. Before you trust a set of results, scan them for the point that breaks the run and ask what could have caused it.
Spot the reading that hints at an error
A group weighed the same 50-gram mass on a balance six times to check it was reliable. The readings should barely change. Click the one that signals something went wrong.
Click the point that does not fit the pattern of the others.
An untested assumption can quietly spoil a fair test
When you analyse a method, look hard at what was allowed to vary. If more than one thing changed between groups, the conclusion rests on the assumption that the extra changes did not matter, and that assumption is often wrong. Work through the setup below and hold every variable steady except the one being tested to see whether the comparison was actually fair.
Was only one thing allowed to change?
A report claims a sports drink keeps people more alert than water during exercise. Volunteers drank one or the other, then rated their alertness. Decide which variables the method should have held the same.
To trust the claim, the drink should be the only difference between the two groups. Anything else that changes gives an alternative explanation for the result, which is an unexamined assumption in the method.
Variable being tested: The drink: sports drink or water (this one we change)
How long and how hard each group exercised
The time of day the alertness was rated
How much sleep the volunteers had the night before
The temperature of the room they exercised in
Not a fair test yet: more than one thing is changing, so you could not tell which change caused the result. Hold every other variable the same.
Why this matters
Every day you meet claims backed by numbers, in advertisements, news and online posts. Asking what the evidence actually shows, what was assumed, and what could have gone wrong protects you from being misled. The same habit lets scientists improve the work of others rather than simply trust it.
Quick self-check
1. A group tested one paper towel brand once and concluded it is the most absorbent on the market. What is the main weakness?
2. Which of these is a possible source of error rather than a finding?
3. An experiment assumes the room stayed the same temperature all day, but the heater switched off at lunch. This is best described as a...
4. Two groups testing the same question get opposite results. The most scientific response is to...
5. A report claims a fertiliser boosts growth but never says how much was used or how plants were chosen. These gaps are best called...