7.5Reactivity 3 — What are the mechanisms of chemical change?
Inquiry 2 & 3: Collecting, Processing, Concluding and Evaluating — IB Chemistry SL Revision Notes & Topical Questions
10 min read 12 parts Lesson 9 of 12 Unit quiz
Lesson summary
7.5 Inquiry 2 & 3: Collecting, Processing, Concluding and Evaluating — IB SL Notes — IB CHEMISTRY SL notes: Record and process raw data with uncertainties, interpret graphs, draw evidence-based conclusions and evaluate errors with realistic improvements.Raw data table — the first…
- Table standards
- Sufficient data
- Recording rule
- Importance
- Colour changes
- Gas evolution
Part 1 of 10Collecting quantitative data
Raw data table
- Raw data table the first table, containing only direct measurements as taken, with no calculation, rounding or averaging.
Table standards
- Table standards a specific numbered title, labelled columns for the independent and dependent variables,
- and the quantity, symbol, units and absolute uncertainty in the column header, not in the body.
Precision
- Precision every value to the precision of the instrument, e.g. a burette to two decimal places (24.50 cm3, last digit 0 or 5) and a two-decimal balance as 5.00 g.
Sufficient data
- Sufficient data at least five increments of the independent variable and at least three repeats of each.
Recording rule
- Recording rule never calculate in the head or on scrap paper;
- record initial and final burette readings, not just the titre.
Part 2 of 10Collecting qualitative data
Importance
- Importance qualitative observations give context and evidence for explaining unexpected results and errors.
Colour changes
- Colour changes of solutions and precipitates, e.g. a persistent pale pink at a titration end point.
Gas evolution
- Gas evolution effervescence, odour and rate of bubbling.
Temperature
- Temperature noticeable changes such as a warm beaker.
Precipitates and soot
- Precipitates and soot formation of a precipitate, or soot showing incomplete combustion.
Part 3 of 10Addressing problems during collection
1Principle
- Principle notice problems as they happen, record them and respond.
2Reaction too fast or too slow
- Reaction too fast or too slow adjust the concentration and record the change and reason.
3Outlier
- Outlier record it, do an extra trial to get concordant results and justify its exclusion later without erasing it.
4Hard-to-judge end point
- Hard-to-judge end point note the gradual colour change as a limitation to discuss in the evaluation.
Part 4 of 10Processing data
1
Layout
- Layout show raw and processed data in separate, labelled tables, and show one full worked calculation of each type.
2
Mean
- Mean for repeats, x̄ = Σxi ÷ N, and for titrations average only concordant titres (within ±0.10 cm3), excluding the rough and any anomalous titre.
3
Exclusion
- Exclusion clearly anomalous points are excluded with a stated justification.
4
Addition and subtraction
- Addition and subtraction add absolute uncertainties, so a titre from two ±0.05 cm3 readings has ±0.10 cm3.
5
Multiplication and division
- Multiplication and division add percentage uncertainties, as for n = c × V.
6
Reciprocal
- Reciprocal rate = 1/t has the same percentage uncertainty as t.
7
Percentage uncertainty
(absolute uncertainty ÷ measured value) × 100.
8
Reporting
- Reporting give the uncertainty to 1 significant figure, quote the result to the same decimal places, show units,
- and derive units for new quantities such as rate in s−1.
9
Significant figures
- Significant figures the answer has the same number of significant figures as the least precise data, not the full calculator display.
Part 5 of 10Graphs
Graphs
Required features
- Required features a specific title, the independent variable on x and the dependent variable on y, labelled axes with units, linear scales using at least half the grid,
- and clearly plotted points.
Line of best fit
- Line of best fit a single smooth line or curve showing the trend, with points roughly balanced on either side, not dot-to-dot.
Gradient
- Gradient can represent a key value, such as the rate constant k from a rate against concentration graph for a first-order reaction.
Y-intercept
- Y-intercept the value of the dependent variable when the independent variable is zero.
Area
- Area can represent a total, as under a Maxwell-Boltzmann curve.
Error bars
- Error bars large bars indicate large random error, and a line passing through all bars indicates a good fit.
Part 6 of 10Interpreting results
1Two steps
- Two steps describe the trend, then explain it with chemistry.
2Describing
- Describing use terms such as linear positive correlation, inversely proportional, exponential increase and plateau.
3Explaining
- Explaining link to collision theory, transition state theory, Le Chatelier's principle or intermolecular forces.
4Anomalies
- Anomalies highlight them on the graph and justify them with a likely cause, such as a delay in starting the stopwatch.
5Combined evidence
- Combined evidence qualitative and quantitative data together, e.g. soot explains a low enthalpy of combustion.
| Term | Meaning | Linked to |
|---|---|---|
| Accuracy | Closeness to the accepted value, needing a literature value | Systematic errors |
| Precision | Closeness of repeats to each other | Random errors |
| Reliability | Consistent, concordant repeats | Repeat trials |
| Validity | A fair test with other variables controlled | Method design |
Part 7 of 10Writing the conclusion
1Direct answer
- Direct answer start by answering the research question using the trend found in the analysis.
2Evidence
- Evidence include key processed data such as the final concentration, gradient or enthalpy change, with its uncertainty.
3Hypothesis
- Hypothesis state explicitly whether the results support or refute it, and a refuted hypothesis is a valid finding, not a failed experiment.
4Science context
- Science context relate the findings to chemical principles and compare with a literature value from the IB Data Booklet or a reliable source, citing it.
5Percentage error
|(experimental − literature)| ÷ literature × 100.
6Error against uncertainty
- Error against uncertainty if the error is no larger than the total percentage uncertainty, random measurement uncertainty explains the difference;
- if it is larger, significant systematic error was present.
7Within range
- Within range if the literature value lies within the experimental uncertainty, the result is consistent with it.
8Discipline
- Discipline be concise, add no new ideas and claim only what the data shows, stating when data are inconclusive.
Part 8 of 10Random and systematic errors
| Feature | Random error | Systematic error |
|---|---|---|
| Cause | Unpredictable fluctuations, human reaction time | Faulty set-up, uncalibrated apparatus, wrong assumption |
| Effect | Scatter around the line of best fit | Results consistently too high or too low |
| Reduced by | More repeats and averaging | Changing the method or apparatus; averaging does not help |
| Examples | Stopwatch timing, judging an end point, reading a meniscus | Heat loss, an uncalibrated balance, incomplete combustion, evaporation of a volatile reagent |
Random and systematic errors
Weaknesses
- Weaknesses aspects of the method causing significant error, such as using glass instead of polystyrene in calorimetry.
Limitations
- Limitations factors that limit the scope of the conclusion, such as having tested only a few alcohols.
Assumptions
- Assumptions simplifications that are not perfectly true, such as taking solutions to have the specific heat capacity and density of water.
Part 9 of 10Evaluating the investigation
1Hypothesis
- Hypothesis evaluate the strength of its support in the light of scatter and errors.
2Prioritise
- Prioritise focus on the one or two errors with the largest impact, such as heat loss in calorimetry rather than balance precision.
3Be specific
- Be specific avoid blaming human error and name the source, e.g. parallax in reading a measuring cylinder meniscus.
4Close the loop
- Close the loop for each weakness explain its impact on the result and then give an improvement.
5Improvements
- Improvements must be relevant and realistic in a school laboratory,
- such as a lid and draught shield for heat loss, a more precise balance or probe,
- or an automated sensor instead of visual end-point timing.
6Unrealistic examples
- Unrealistic examples a bomb calorimeter is not a realistic improvement for a school.
7Own data
- Own data refer to your own results, graphs and observations rather than writing a generic evaluation.
8Extensions
- Extensions new questions building on the findings, such as other homologous series, a wider temperature range or different catalyst surface.
Part 10 of 10More from the specification
1
Interpretation
- Interpretation describe the overall trend in the data and explain it using scientific theory, such as collision theory for rate
2
Processing data
- Processing data calculate averages, plot graphs with best-fit lines and propagate uncertainties
3
Sodium thiosulfate
- Sodium thiosulfate reacts with acid to give a sulfur precipitate, so the time for a cross to disappear measures the rate
4
Evaluation
- Evaluation assess systematic and random errors, such as vinegar concentration variation in a titration, and suggest improvements and further work
5
Interpretation
- Interpretation describe the overall trend in the data and explain it using scientific theory, such as collision theory for rate
6
Processing data
- Processing data calculate averages, plot graphs with best-fit lines and propagate uncertainties
7
Sodium thiosulfate
- Sodium thiosulfate reacts with acid to give a sulfur precipitate, so the time for a cross to disappear measures the rate
8
Evaluation
- Evaluation assess systematic and random errors, such as vinegar concentration variation in a titration, and suggest improvements and further work
9
Vinegar
- An ethanoic acid solution of about 5%.
Key terms
| Term | Meaning |
|---|---|
| Absolute uncertainty | The ± margin on a measurement, in the same units. |
| Percentage uncertainty | The absolute uncertainty divided by the measurement × 100. |
| Concordant results | Titres agreeing within ±0.10 cm3. |
| Percentage error | The difference between experimental and literature value as a percentage of the literature value. |
| Random error | Unpredictable variation causing scatter, reduced by repeats. |
| Systematic error | A consistent bias in one direction that averaging does not remove. |
| Validity | Whether the method was a fair test with other variables controlled. |
Avoid these mistakes
1
Putting units or uncertainties in every table entry — they belong in the column header.
2
Averaging the rough titre or an anomalous result — use only concordant titres.
3
Claiming repeats reduce systematic error — they only reduce random error.
4
Offering be more careful as an improvement — give a specific change to the apparatus or method.
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