Reactivity 3 — What are the mechanisms of chemical change?
Inquiry 3: Concluding and evaluating — IB Chemistry HL Revision Notes & Topical Questions
10 min read 17 parts Lesson 11 of 11 Unit quiz
Lesson summary
Inquiry 3: Concluding and evaluating (Part 1) - Concluding — IB CHEMISTRY HL notes: Write a concise conclusion that answers the research question using the data, tests the hypothesis, compares with accepted values and comments on uncertainty.A conclusion is a brief, focused summa…
- Percentage error
- Percentage uncertainty
- Literature value
- Systematic error
- A conclusion is a brief, focused summary of the findings
- It gives a clear, concise answer to the research question, based only on the evidence from the data analysis
Section 1 of 17Purpose of a conclusion
Purpose of a conclusion
A conclusion is a brief, focused summary of the findings.
It gives a clear, concise answer to the research question, based only on the evidence from the data analysis.
A strong conclusion relates the findings to broader scientific principles and to accepted values.
Section 2 of 17Justifying the conclusion with data
1
Begin with a direct answer to the research question.
2
Use the trend found in the interpretation for a definitive statement.
3
Quote key processed results as evidence.
- The final calculated concentration.
- The gradient of the graph.
- The experimental enthalpy change.
4
Never introduce new ideas or explanations in the conclusion, because it only summarises the analysis already done.
5
Only conclude what the data shows, and state it if the data is inconclusive.
6
Always give the final result with its uncertainty.
Section 3 of 17Relating the outcome to the hypothesis
1
State explicitly whether the results support or refute the hypothesis.
2
A result that does not support the hypothesis is a valid scientific finding and does not mean the experiment failed.
3
Reasons for any discrepancy are suggested in the evaluation.
4
A conclusion can link the finding to theory, for example a linear rate–concentration graph through the origin is consistent with collision theory.
Section 4 of 17Comparing with accepted scientific context
A high-level conclusion compares the result with accepted values from the IB data booklet, textbooks or other reliable sources.
The comparison allows a comment on the accuracy of the outcome.
When comparing:
- State the literature value.
- Cite the source.
- Quantify the difference, usually by calculating the percentage error.
Percentage error
(accepted value − experimental value) ÷ accepted value × 100, and the word "close" alone is not enough.
Section 5 of 17Impact of uncertainty on the conclusion
1
The propagated uncertainty sets the confidence in the final result, so its significance must be commented on.
2
Compare the percentage uncertainty with the percentage error.
- If the percentage error is larger than the percentage uncertainty, the deviation is not explained by instrument limits alone.
- In that case significant systematic errors in the method are likely.
3
If the literature value lies within the range of the experimental uncertainty, the result is consistent with the accepted value.
4
The result 50 ± 5 kJ mol−1 is consistent with an accepted value of 53 kJ mol−1 for this reason.
Section 6 of 17Style of a conclusion
Style of a conclusion
Be concise and direct, as a short paragraph and not a long essay.
Quantify comparisons instead of using vague words.
Section 7 of 17More from the specification
More from the specification
Evaluating Experimental Weaknesses
- Critical evaluation involves identifying specific procedural weaknesses in an investigation,
- such as incomplete combustion and uninsulated heat loss to the surroundings.
Impact of Random and Systematic Errors
- Distinguishing between random errors affecting measurement precision and systematic errors that consistently skew calculated enthalpy values.
Realistic Methodological Improvements
- Proposing realistic, specific experimental modifications to improve calorimetry accuracy.
- (e.g. using a bomb calorimeter with oxygen flow or copper draught shields)
Drawing Valid Empirical Conclusions
- Formulating evidence-based conclusions that directly evaluate whether experimental data for the combustion of alcohols support the original scientific hypothesis.
Evaluating Experimental Weaknesses
- Critical evaluation involves identifying specific procedural weaknesses in an investigation,
- such as incomplete combustion and uninsulated heat loss to the surroundings.
Impact of Random and Systematic Errors
- Distinguishing between random errors affecting measurement precision and systematic errors that consistently skew calculated enthalpy values.
Realistic Methodological Improvements
- Proposing realistic, specific experimental modifications to improve calorimetry accuracy.
- (e.g. using a bomb calorimeter with oxygen flow or copper draught shields)
Drawing Valid Empirical Conclusions
- Formulating evidence-based conclusions that directly evaluate whether experimental data for the combustion of alcohols support the original scientific hypothesis.
Random error
- Unpredictable variation causing scatter, reduced by repeats.
Primary alcohol
- An alcohol in which the C–OH carbon is attached to one other carbon.
Improvement
- A specific change that addresses an identified weakness.
Weakness
- A feature of the method that leads to significant error.
Alcohol
- A compound containing –OH with a name ending in -ol.
Combustion
- Burning in oxygen
Limitation
- A factor that restricts the scope within which a conclusion is valid.
Calorimeter
- An insulated container used to measure temperature changes in reactions.
Section 8 of 17Purpose of an evaluation
1
The evaluation is a critical reflection on the methodology.
2
It identifies the weaknesses and limitations of the investigation.
3
It assesses the quality of the data and its effect on the conclusion.
4
It suggests meaningful, realistic improvements.
Section 9 of 17Evaluating the hypothesis
Evaluating the hypothesis
The comment on the hypothesis follows from the conclusion.
Even when the data supports the hypothesis, the strength of the support is judged using the uncertainties and errors found.
A large scatter of points around the line of best fit suggests random error, so the support is weaker.
Section 10 of 17Systematic errors
Systematic errors
Systematic error
Systematic error a flaw in the method or apparatus that makes results consistently wrong in the same direction.
Heat loss to the surroundings in calorimetry makes the temperature change too small, so ΔH is always less exothermic.
A badly calibrated balance that reads 0.2 g too high is another systematic error.
Repeating trials does not reduce systematic error.
Section 11 of 17Random errors
Random error
Random error an unpredictable variation in measurements that scatters results around the true value.
Subjective judgement of a titration end point by eye is a random error.
Fluctuation in reading a burette meniscus is a random error.
Random errors are reduced by taking repeat trials and calculating an average.
Section 12 of 17Weaknesses, limitations and assumptions
Weakness
Weakness an aspect of the method that causes significant systematic or random error.
- A glass beaker used instead of a polystyrene cup in calorimetry allows heat loss.
Limitation
Limitation a factor that limits the scope of the conclusion and defines where it is valid.
- A study of only the first four primary alcohols cannot support a linear trend for longer chains.
Assumption
Assumption a simplification in the calculations that is not perfectly true.
- Assuming that solutions have the specific heat capacity and density of pure water introduces a small inaccuracy.
Section 13 of 17Realistic and relevant improvements
1
Suggest a specific improvement for every significant weakness or source of error.
2
The improvement must be relevant, which means it directly addresses the weakness identified.
3
The improvement must be realistic, which means it can be carried out in a typical school laboratory.
- A bomb calorimeter is not a realistic improvement for a school.
4
Typical improvements:
- An insulating lid and a draught shield reduce heat loss in calorimetry.
- A systematic adjustment of burner distance helps achieve complete combustion.
- A wider range of the independent variable or more isomers extends the investigation.
Section 14 of 17Structure of a strong evaluation
1
For each weakness, link the weakness to its impact on the result and then to a specific improvement.
2
Be specific and never blame "human error".
- Name the source, such as parallax error when reading a meniscus of a measuring cylinder.
3
Prioritise the one or two errors with the biggest effect on the final result.
4
Refer to the investigation's own results, graphs and observations.
- Large error bars at high temperatures suggest that rapid reaction times were hard to measure, which reduced precision.
Section 15 of 17More from the specification
More from the specification
Evaluating Percentage Error Against Literature Values
- In scientific inquiry, experimental outcomes are evaluated by calculating percentage error relative to accepted literature values: % error = (|experimental - literature| / literature) × 100%.
- (such as enthalpy changes)
Systematic Errors in Calorimetry Experiments
- Discrepancies in measured enthalpy of neutralisation or combustion typically arise from systematic heat loss to surroundings and incomplete reactions, rather than variations in solution concentration alone.
Scientific Justification of Methodological Improvements
- A thorough experimental evaluation discusses specific procedural modifications—such as using a bomb calorimeter or vacuum-jacketed cup—to minimize heat loss and improve experimental accuracy.
Addressing Research Questions with Evidence
- Formulating a robust conclusion requires synthesizing quantitative experimental data, examining error margins,
- and discussing whether the empirical results support the underlying scientific hypothesis.
Concentration
- Moles ÷ volume in dm³, or g/dm³.
Percentage error
- The difference between experimental and literature value as a percentage of the literature value.
Percentage uncertainty
- The absolute uncertainty divided by the measured value, multiplied by 100.
Section 16 of 17Key terms
| Term | Meaning |
|---|---|
| Conclusion | A statement answering the research question using the data. |
| Percentage error | The difference from an accepted value as a percentage of that value. |
| Percentage uncertainty | Absolute uncertainty as a percentage of the measured value. |
| Literature value | An accepted value from a reliable source such as the data booklet. |
| Systematic error | A flaw that shifts all results in one direction. |
| Hypothesis | A testable prediction with a scientific justification. |
| Term | Meaning |
|---|---|
| Systematic error | An error that shifts all results in one direction and is not reduced by averaging. |
| Random error | An unpredictable error that causes scatter and is reduced by repeats. |
| Weakness | A feature of the method that leads to significant error. |
| Limitation | A factor that restricts the scope within which the conclusion is valid. |
| Assumption | A simplification made in calculations that is not perfectly true. |
| Evaluation | A critical assessment of the method, data and conclusion. |
Section 17 of 17Exam technique
1
Introducing new ideas or explanations in the conclusion — it only summarises the analysis already done.
2
Claiming more than the data shows — if the data is inconclusive, say so.
3
Saying the experiment "failed" when the hypothesis is not supported — an unsupported hypothesis is a valid finding.
4
Describing a result as "close" to the literature value without calculating the percentage error.
5
Leaving out the uncertainty of the final result.
1
Writing a generic evaluation that could apply to any experiment.
2
Listing "human error" without saying what the error was and how it affected the result.
3
Suggesting an improvement that does not address the identified weakness.
4
Proposing an unrealistic improvement for a school laboratory.
5
Believing repeats reduce systematic error — they reduce only random error.
Last updated: