A conclusion is short. That surprises people who have just spent three weeks on an investigation, but its job is narrow: answer the research question, back the answer with your own numbers, and say how much confidence the data supports. Nothing new goes in here — if it was not in your analysis, it does not belong.
📘 What you need to know
Open with a direct answer to your research question.
Support it with key processed values — the optimum, the intercept, the values at each end of your range.
State plainly whether the results support or refute your hypothesis. Refuting it is a real finding, not a failure.
Compare with the accepted scientific context, citing your source, and quantify the gap with percentage error where you can.
Use the spread of your data to say how confident you are. Overlapping error bars mean a weaker claim.
No new explanations and no claims your data cannot carry.
What goes in, and in what order
A conclusion has a shape. Follow it and you will not leave anything out, and you will not drift into evaluation, which is a separate section with its own marks.
If you find yourself explaining something for the first time in the conclusion, it belonged in the interpretation. If you find yourself criticising your method, it belongs in the evaluation.
Answer the question you actually asked
Go back to your research question and answer it in the first sentence, using the same variables and the same units. Then give the numbers that prove it — the optimum from your graph, the value at each end of the range, the intercept if there is one.
WORKED EXAMPLE
Write a conclusion for the amylase investigation: rate of starch breakdown measured at 10, 20, 30, 40 and 50 °C, with a peak rate of 0.0167 s−1 at 40 °C.
1. The direct answer
Temperature has a clear effect on the rate of starch breakdown by amylase: the rate rises to a maximum and then falls.
2. The evidence, in numbers
The rate increased from 0.00412 s−1 at 10 °C to a maximum of 0.0167 s−1 at 40 °C, then fell to 0.00758 s−1 at 50 °C. The optimum temperature was therefore 40 °C.
3. The hypothesis
This supports the hypothesis that activity would rise to an optimum and then fall as the enzyme denatured.
4. The scientific context
An optimum near body temperature is consistent with amylase functioning in a mammalian digestive system.
5. Confidence
Standard deviations were small (1.4–4.0 s) and error bars at 30 and 40 °C did not overlap, so the position of the optimum is well supported.
Answer, evidence, hypothesis, context, confidenceFive sentences and it is done. Notice there is no mention of what went wrong — that is the evaluation’s job.
Supported or refuted? Say it plainly
Your hypothesis made a prediction with a reason attached. The conclusion has to state whether your data backed it. Students hedge here, and hedging costs marks.
A refuted hypothesis is not a failed experiment. It is a genuine result, and stating it clearly shows more scientific maturity than quietly rewording your prediction to match what you found. You then suggest reasons for the mismatch — in the evaluation, not here.
WORKED EXAMPLE
A student predicted the optimum would be 50 °C. The data gave 40 °C. How should the conclusion handle this?
State the mismatch directly
The results do not support the hypothesis. The predicted optimum was 50 °C, but the highest rate was recorded at 40 °C, with the rate at 50 °C less than half the peak value.
Say what the data does show
Activity had already begun to fall by 50 °C, indicating denaturation was under way at a lower temperature than predicted.
What not to do
Do not write that the optimum was “roughly 40 to 50 °C” to make the prediction look right. Your 50 °C rate is clearly below the 40 °C rate, and the error bars do not overlap.
Hypothesis refuted — stated clearly, with the evidenceThe reasons why the prediction was wrong go in the evaluation. Here you only report what happened.
Compare with the accepted science
A conclusion that stops at your own data is fine. One that measures itself against what is already known is better, because it lets you say something about accuracy.
State the expected pattern or published value before comparing.
Cite the source — a textbook, the data booklet, a paper. “It says online” is not a citation.
Quantify the gap with a percentage error where a specific value exists.
Percentage error
(your value − accepted value) ÷ accepted value × 100
Percentage error only works where a single accepted value exists. For many biological questions there is no such number, so compare against the expected pattern instead.
Watch the wording: percentage error compares you to an accepted value, while percentage uncertainty comes from your equipment. They are different numbers doing different jobs, and swapping them is a common slip.
How far can you push the claim?
The spread of your data sets a ceiling on what you are allowed to say. Small, non-overlapping error bars let you make a firm claim. Wide or overlapping ones do not — and if the data cannot settle the question, saying so is the correct answer.
Overclaiming is the more expensive mistake. A firm statement your error bars cannot support reads as though you did not look at your own graph.
💡 Exam tip
Answer the research question in the first sentence, using its own wording.
Quote your key processed values — the optimum, the intercept, the endpoints — with units.
Say “supports” or “does not support” the hypothesis. One clear sentence.
Name and cite the published value or expected pattern you are comparing against.
Calculate percentage error where a single accepted value exists.
Tie your level of confidence to the standard deviations and error bars you actually have.
Keep it to a paragraph, and keep criticism of the method out of it.
⚠ Common mix-up
Introducing a new explanation that never appeared in the analysis.
Drifting into evaluation and listing what went wrong.
A conclusion with no numbers in it, so nothing connects it to your data.
Rewording the hypothesis after the fact so it matches the result.
Claiming a difference the error bars will not support.
Confusing percentage error with percentage uncertainty.
Comparing to “what scientists say” with no actual source named.
Generalising beyond what you tested — one enzyme, one organism, one range.
Up next: Evaluating the Method — identifying the real weaknesses in your procedure, tracing what each one did to your result, and suggesting fixes that would actually work.
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