Practical Skills 2: Paper 5

0. What Examiners Want

This guide covers the skills tested in Paper 5 (Planning, Analysis and Evaluation) of Cambridge International AS & A Level Chemistry 9701 (2025–27). The practical section of the syllabus lists expectations for each skill, so the table below gives them word for word, grouped by skill, with the section where each is taught. It builds on Paper 3 (previous unit), where the measurements and graph conventions are set out.

1. The Paper and the Planning Frame

Paper 5 is a written paper of 30 marks with two or more questions. No laboratory is needed, but you must be able to picture the apparatus and the procedure exactly. Information that you are not expected to know is given in the paper.

Planning and evaluation each carry about 40% of the paper. Marks are allocated to skills, not to topics.

Define the problem first. Every plan starts with an aim, a prediction and the variables. Use the same frame every time.

State the three kinds of variable and how each will be handled. A prediction is a statement plus a reason.

Hypothesis. A prediction becomes a hypothesis when you give the reason. For example: ‘the initial rate is directly proportional to the concentration of HCl, because doubling the concentration doubles the number of H⁺ ions per unit volume, so the frequency of successful collisions with the Mg surface doubles.’ You may be asked to show the prediction as a sketch graph.

Draw the shape, label both axes with quantity and unit, and mark any intercept or plateau. Always add the reason.

2. Writing a Method

A method is marked as a procedure that another student could follow without asking you anything. The syllabus lists what it must contain: quantities, apparatus, steps, how to vary and measure variables, repeats, a table, how to use the data and the risks. Write it as numbered steps or a flowchart, and add a labelled diagram if the set-up is complicated.

A numbered plan. Each box gives quantities, apparatus and the order. The last box says how the data are used.

Choose suitable quantities. The amount of reagent must suit the apparatus. For a gas, calculate the mass of solid that gives a little less than the capacity of the syringe or burette.

Use excess acid so the solid is limiting, then choose its mass to give about 90% of the apparatus capacity.

Worked example. In the plan in the flowchart, 0.05 g of magnesium reacts with excess hydrochloric acid, Mg + 2HCl → MgCl₂ + H₂. Show that the volume of hydrogen fits a 100 cm³ gas syringe, and that 50.0 cm³ of 0.50 mol dm⁻³ HCl is in excess. [3]

Moles of Mg = 0.05 ÷ 24.3 = 2.06 × 10⁻³ mol [1]. Moles of H₂ = 2.06 × 10⁻³, so the volume = 2.06 × 10⁻³ × 24 000 = 49 cm³, less than 100 cm³ [1]. Moles of HCl = 0.50 × 50.0 ÷ 1000 = 0.025 mol, which is far more than the 2 × 2.06 × 10⁻³ = 4.1 × 10⁻³ mol needed, so the acid is in excess [1].

Standard solutions and titrations. Plans often include making a solution of known concentration or a titration. State the steps in order, with the apparatus.

Calculate the mass, weigh by difference, dissolve completely, transfer with rinsings, make up to the mark and mix.

Worked example. Calculate the mass of ethanedioic acid dihydrate, H₂C₂O₄•2H₂O (M_r = 126.0), needed to make 250 cm³ of a 0.0500 mol dm⁻³ standard solution. [2]

Moles = 0.0500 × 250 ÷ 1000 = 0.0125 mol [1]. Mass = 0.0125 × 126.0 = 1.58 g (weigh by difference to 0.01 g) [1].

Weighing by difference. Weigh the container with the solid, tip the solid into the vessel, weigh the container again, and subtract. The results table has three columns: mass of container and solid / g, mass of container after transfer / g and mass of solid transferred / g.

Choose appropriate apparatus. Compare the percentage error of each measurement and choose the instrument that suits the size of the quantity. A measuring cylinder is adequate for 25–50 cm³ of acid in an enthalpy experiment, but a balance is poor for 0.10 g of magnesium.

A small quantity gives a large percentage error. Choose the instrument by the size of the quantity being measured.

Risk assessment. Name the specific hazard and give the matching precaution. The question may state whether a substance is harmful, corrosive or toxic.

Link each hazard to one precaution. ‘Be careful’ is not a precaution.

Results tables. Include a column for every raw quantity and for every derived quantity, with quantity / unit headings, and a row or column for each repeat. Show how a derived value is obtained, for example (1 ÷ t) / s⁻¹.

Using the data. End the plan by saying what you will plot or calculate and how it tests the prediction: ‘plot the initial rate against concentration; if the line is straight through the origin, the rate is directly proportional to concentration.’

3. Dealing with Data

You are given results and must decide what to calculate, what to plot and what the graph shows. First process the raw data into derived results (mass of solid, titre, rate, 1 ÷ t) with units and sensible significant figures: the same as, or one more than, the data provided.

Choose what to plot. Rearrange the equation so that it has the form y = mx + c, then plot y against x. A straight line shows the relationship; the gradient and intercept give the quantities.

Compare the equation with y = mx + c to decide what to plot and what the gradient means.

Worked example. The graph shows ln k against 1/T for a reaction. Use it to find the activation energy. R = 8.31 J K⁻¹ mol⁻¹. [3]

The gradient is read from two points on the line, far apart. E_a = −gradient × R.

ln k = ln A − (E_a/R)(1/T), so the gradient is −E_a/R [1]. Gradient = (0.26 − (−0.64)) ÷ ((2.95 − 3.10) × 10⁻³) = 0.90 ÷ (−0.15 × 10⁻³) = −6.0 × 10³ K [1]. E_a = 6.0 × 10³ × 8.31 = 5.0 × 10⁴ J mol⁻¹ = 50 kJ mol⁻¹ [1].

Cooling-curve extrapolation. When a reaction is slow or heat is lost, the highest temperature measured is lower than it would be for an instantaneous reaction. Take temperature readings before mixing, mix at a stated time, continue recording, and extrapolate the cooling line back to the time of mixing.

Extrapolate the line of the cooling points back to the time of mixing and read the temperature there.

Worked example. In the graph, 50.0 cm³ of acid containing 0.0500 mol of HCl is added to a solid at 3.0 minutes. Use the extrapolated temperature rise of 14.2 °C to calculate ΔH. [3]

q = m × c × ΔT = 50.0 × 4.18 × 14.2 = 2968 J [1]. ΔH = −2968 ÷ 0.0500 = −59 400 J mol⁻¹ [1] = −59.4 kJ mol⁻¹, negative because the temperature rose [1].

Percentage error and apparatus. Compare the percentage errors of the different measurements; the largest one limits the experiment.

A small mass or a small temperature change gives the largest percentage error. Improve that first.

4. Evaluation

Evaluation questions ask you to judge the quality of the experiment: the anomalies, the repeats, the range, the control of variables, the apparatus and the method. Answer with specific points from the data, not general statements.

Anomalies. An anomalous result does not fit the pattern. Label it, give a possible cause and say how to deal with it: repeat it and, in drawing a line of best fit, ignore it.

An anomalous point lies away from the line of best fit. Give its position, a cause and how to deal with it.

Repeats and range. Results are reliable if repeats agree (for example titres within 0.10 cm³) and the points lie close to the line of best fit. Say whether the data are replicated, and the benefit: a mean reduces the effect of random errors and shows an anomaly. The range is adequate when it covers the interesting region; to test the trend, extend it above or below the existing values or take more readings near a changing gradient or an inflexion point.

Control of variables. Use the data to judge whether a variable was controlled. For example, if the starting temperatures differ by 3 °C in an experiment on the effect of concentration, the temperature was not effectively controlled, and the rate differences may not be due to concentration alone.

Weaknesses and improvements. Give the weakness, why it matters and a specific change. Decide which is the main weakness by comparing the sizes of the errors.

5. Conclusions

A conclusion describes the key features of the data, says whether they support the prediction, and gives the chemical reason. Quote the numbers.

Worked example. A student predicts that the rate is directly proportional to the concentration of thiosulfate. A graph of 1 ÷ t against concentration is a straight line through the origin with a gradient of 0.500 dm³ mol⁻¹ s⁻¹ and one anomalous point. State a conclusion. [3]

The line of best fit is straight and passes through the origin, so 1 ÷ t, and therefore the rate, is directly proportional to the concentration [1]. The prediction is supported (apart from the anomalous point, which was ignored) [1]. This is because doubling the concentration doubles the frequency of collisions, so the rate doubles [1].

Comments on quality. Use these ideas. Data are valid if they can prove or disprove the prediction, and reliable if repeats agree and no points are anomalous. If an error in a result is small compared with the measurement uncertainty, it can be explained by measurement error; if it is much larger, other factors (heat loss, incomplete reaction) must be responsible.

Further predictions. A good conclusion ends by asking a new question that can be tested: ‘the same experiment with a different acid’, ‘the rate at a higher temperature’ or ‘the effect of a catalyst’, with the expected result and its reason.

6. Quick Sheet and Checklist

• [ ] I can write a hypothesis with a reason and sketch the predicted graph with labelled axes.
• [ ] I can name the independent, dependent and controlled variables and explain how each is varied, measured or kept constant.
• [ ] I can write a numbered plan with quantities, apparatus, repeats, a results table and the use of the data.
• [ ] I can calculate a mass or volume that suits the apparatus, and describe how to make a standard solution.
• [ ] I can give the hazard and the matching precaution for a stated substance or procedure.
• [ ] I can process data to find a mean, a percentage loss or gain and a percentage error, and plot y = mx + c to find a gradient and intercept.
• [ ] I can extrapolate a cooling curve, identify an anomaly and explain it, and judge the range and the replicates.
• [ ] I can state the main weakness of a procedure with a specific improvement, and write a conclusion that says whether the data support the prediction.