Science · Inquiry & data skills
Identifying Sources of Error
When a result looks strange, a scientist does not panic, they ask where the error came from. Here is how to track it down.
The CAEC Science test does not ask you to memorize facts. It hands you investigations and checks whether you can think them through. One question it loves: an experiment produced an odd or unexpected result, what most likely went wrong?
You do not need to be a scientist to answer this. You need to know the handful of places error usually hides, and a habit of matching the clue in the scenario to the right one. Let's build that skill, then practise it on real-looking data.
Four places error hides
Almost every error in a school-level investigation falls into one of four buckets. Learn the buckets and a scenario question becomes a matching exercise.
- Instrument / measurement error. The tool itself is off, an uncalibrated thermometer, a stretched ruler, a scale that does not read zero when empty. The tell-tale sign is a steady offset: every reading is wrong by about the same amount.
- Human error. The person makes a mistake, misreading a scale, recording a number wrong, or using inconsistent technique from trial to trial. This often shows up as one reading that jumps away from the others.
- Environmental factors. Something in the surroundings interferes, a draft, a change in room temperature, sunlight warming a sample, vibration. These nudge results in ways that have nothing to do with what is being tested.
- Sampling error. The group you measured does not represent the whole. A sample that is too small, or biased toward one kind of person or thing, gives a result you cannot generalize.
Matching the clue to the source
Each source of error leaves a different fingerprint in the data. Keeping these straight makes the questions much easier.
| Source of error | Clue in the scenario | How to check or fix it |
|---|---|---|
| Instrument | Every reading is off by the same steady amount. | Test the tool against a known value; calibrate or replace it. |
| Human | One reading jumps away from the rest, or technique varies. | Repeat the trial, read carefully, use the same method each time. |
| Environmental | Results drift when conditions (heat, draft, light) change. | Control the surroundings; keep conditions the same for every trial. |
| Sampling | The group measured is tiny or one-sided. | Use a larger, more representative sample. |
Worked example: the thermometer that reads too high
Here is the kind of scenario the test gives you. Read it, then we will track down the error.
A learner boils pure water at sea level and measures its temperature five times with the same thermometer. Pure water should boil at about 100°C. Every single trial reads close to 102°C.
Here are the recorded trials. Notice how consistent they are.
| Trial | Thermometer reading | Expected |
|---|---|---|
| 1 | 102°C | 100°C |
| 2 | 102°C | 100°C |
| 3 | 101°C | 100°C |
| 4 | 102°C | 100°C |
| 5 | 102°C | 100°C |
The pattern is the giveaway. The readings are not scattered, they are consistently about 2°C too high, trial after trial. That steady offset is the fingerprint of an instrument error: the thermometer is reading high because it is out of calibration. If it were human error, you would expect one stray reading, not the same shift every time.
"The water must really boil at 102°C here." This ignores the known boiling point and blames the water instead of checking the tool that gave every reading.
"The thermometer is likely reading about 2°C high. I can check by placing it in ice water, it should read 0°C. If it reads 2°C there too, the instrument is out of calibration."
A second example: the survey at one bus stop
Not every error lives in a measuring tool. Read this one and ask which bucket fits.
A student wants to know whether the whole town supports building a new bus route. She asks 20 people waiting at a single bus stop and finds that 90% are in favour. She concludes that the town overwhelmingly supports the route.
The thermometer was not the problem here, the sample was. People waiting at a bus stop already use buses, so they are far more likely to want better service. The group is both too small and biased toward one viewpoint, so its 90% cannot stand in for the whole town. That is a clear case of sampling error.
The fix is to survey a larger, more representative group, people from different neighbourhoods, including drivers and non-riders, chosen so the sample mirrors the town rather than one corner of it.
Tips for source-of-error questions
- Look at the pattern, not just one number. A steady offset across every trial means the instrument; one stray value means a human slip.
- Compare against a known value. When you can check a tool against something certain (ice water at 0°C, an empty scale at zero), calibration errors become easy to confirm.
- Ask who or what was measured. If the question is about a survey or a group, suspect sampling, check the size and whether it leans one way.
- Scan the surroundings. A draft, sunlight, or a warm room that changed between trials points to an environmental factor rather than the equipment.
Your turn: practice problems
For each scenario, name the most likely source of error and give a reasonable way to check or fix it. Try all three before you reveal.
- A scale reads 0.5 kg even when nothing is on it. A learner weighs several objects and every mass comes out about 0.5 kg heavier than expected. What is the source, and how would you fix it?
- A learner times how long an ice cube takes to melt, but does the first trial near a sunny window and the next two on a cool shelf. The first result is much faster. What kind of error is this?
- To find the favourite sport of an entire school, a student asks only the members of the basketball team. Basketball wins easily. Why is this result untrustworthy?
Tap to reveal the answers
- 1. Instrument error. The scale does not read zero when empty, so it adds a steady 0.5 kg to everything. Fix it by zeroing (taring) or recalibrating the scale, or subtract 0.5 kg from each reading.
- 2. Environmental error. The sunny window warmed the first ice cube, so the conditions, not the ice, differed between trials. Run every trial in the same place at the same temperature.
- 3. Sampling error. The basketball team is a biased sample, they obviously favour basketball, and far too small to speak for the whole school. Survey a larger, mixed group drawn from across the school.
Why this matters for the CAEC
The CAEC Science test is 35 questions in 90 minutes, and a calculator is allowed. It is a skills and scientific-inquiry test, it does not ask you to memorize biology, chemistry, or physics facts. Instead it hands you investigations and asks you to reason about them. Spotting the likely source of an odd result, and explaining how to check it, is exactly the kind of thinking those questions reward.
Want more practice like this? Explore more Science lessons, dig into the CAEC Ready Workbook, or start with a free sample to test yourself.
Disclaimer
This article is a general study lesson. CAEC Ready is an independent study resource and is not affiliated with or endorsed by any government, ministry of education, or official CAEC testing provider.