Science · Inquiry & data skills
Drawing and Applying Conclusions
A good conclusion says exactly what the data shows, no more, no less, and then puts that finding to work in real life.
Here is some good news about the CAEC Science test: it almost never asks you to recall a science fact. Instead it shows you a scenario and some data, then asks you to think like a scientist. One of the skills it leans on most is drawing a conclusion, deciding what a set of results actually proves.
The trick is restraint. The most common wrong answer on these questions is a statement that sounds impressive but claims more than the data can back up. Let's learn how to write a conclusion that stays inside the evidence, and then how to apply it to everyday life or a future investigation.
What a conclusion is (and is not)
A conclusion is a plain-language answer to the original question, built directly on the data you collected. It is not a hope, a hunch, or a grand claim about the whole world. Think of it as a promise you can defend by pointing at your own results.
- It answers the question. Go back to what the investigation set out to find, and respond to that.
- It is supported by the data. Every part of your statement should be something a reader could confirm from the numbers in front of them.
- It stays in bounds. It does not stretch to groups, conditions, or causes that were never actually tested.
Worked example: a plant-growth study
Read the scenario, look at the data, and then we will write a conclusion that fits, and one that does not.
A gardener wants to know whether the amount of daily watering affects how tall a particular variety of bean plant grows. She grows 40 bean plants of the same variety in the same soil and light, splitting them into four groups of 10. Each group gets a different amount of water per day for three weeks. She then records the average height of each group.
| Daily water (mL) | Plants in group | Average height after 3 weeks (cm) |
|---|---|---|
| 50 | 10 | 8 |
| 100 | 10 | 14 |
| 150 | 10 | 21 |
| 200 | 10 | 19 |
Reading the pattern: height climbed from 50 mL up to 150 mL, then dipped slightly at 200 mL. The tallest group was the 150 mL group. Now, what can we honestly conclude?
A valid conclusion vs. one that overreaches
Both statements below come from the same data. One stays inside the evidence; the other quietly invents claims the study never tested.
"More water always makes plants grow taller, so all plants should be given as much water as possible."
- The data does not show "always", the 200 mL group was shorter than the 150 mL group.
- Only one bean variety, soil, and light level were tested, so "all plants" goes far beyond the evidence.
- "As much as possible" is a recommendation the data actively contradicts.
"For this bean variety under these conditions, average height increased as daily water rose from 50 to 150 mL, but did not increase further at 200 mL."
- Every part can be checked against a number in the table.
- It limits the claim to the variety and conditions actually tested.
- It honestly reports the dip at 200 mL instead of ignoring it.
Notice how the supported conclusion uses careful phrases like "for this variety," "under these conditions," and "did not increase further." Those guardrails are not weakness, they are precision.
Four questions that keep a conclusion honest
Before you commit to a conclusion (or pick one on the test), run it past this quick checklist.
- Does the data say this? Point to the row, bar, or trend. If you cannot, cut the claim.
- Am I claiming cause when I only have a pattern? A link between two things is not proof one caused the other unless the experiment was controlled.
- Am I going beyond who or what was tested? Results from one variety, age group, or setting may not apply to others.
- Did I report the whole pattern? Include the dips and exceptions, not just the part that fits a tidy story.
Putting the conclusion to work
Drawing the conclusion is only half the skill. The CAEC also wants to see that you can apply a sound finding, either to an everyday decision or to a sensible next experiment. The key is that your application must stay tied to what the data supported.
"If I grow this same bean variety in similar soil and light, watering around 150 mL a day is a reasonable starting point, more than that did not help in the study." That is a practical takeaway that never leaves the evidence.
"The dip at 200 mL raises a new question: does too much water reduce growth? A follow-up could test amounts between 150 and 250 mL in smaller steps." A good conclusion often points straight to the next experiment.
Your turn: practice questions
Use the same plant-growth study above. Decide what the data supports before you check yourself.
- A reader writes: "Watering with 200 mL kills bean plants." Is this a valid conclusion from the data? Why or why not?
- Which conclusion is better supported, and why? (a) "Among the amounts tested, 150 mL produced the tallest plants." (b) "150 mL is the perfect amount of water for every plant."
- Suggest one reasonable way to apply the finding to a new investigation.
Tap to reveal the answers
- 1. No. The 200 mL group still grew to an average of 19 cm, shorter than the 150 mL group, but clearly alive and growing. The data shows slightly less growth at 200 mL, not death. "Kills" overstates the evidence.
- 2. Statement (a) is better supported. It limits itself to "among the amounts tested," which the table confirms. Statement (b) claims "perfect" and "every plant," neither of which was tested, only four watering amounts and one bean variety were studied.
- 3. Any answer that stays inside the evidence works, for example: test watering amounts between 150 and 250 mL in smaller steps to find where growth peaks, or repeat the study with a different bean variety to see whether the same pattern holds. Both build directly on the supported conclusion.
Why this matters for the CAEC
The CAEC Science test is 35 questions in 90 minutes, and a calculator is permitted, but it rewards inquiry skills, not memorized facts. Knowing which conclusion the data truly supports, and how to apply it without overreaching, is exactly the kind of thinking the test measures again and again.
Want more practice like this? Explore the rest of our Science lessons, pick up the CAEC Ready Workbook for more worked scenarios, 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.