Biology 9700/53 — October/November 2025

Background Concept

To make a solution of a desired concentration from a more concentrated stock solution, use the dilution equation:

where $C_1$ and $V_1$ are the concentration and volume of the stock, and $C_2$ and $V_2$ are the concentration and volume of the diluted solution. The dilution factor is $C_1 / C_2$, and the volume of solvent added is $V_2 - V_1$.

Understanding the Question

You are told that the student has a $0.1\ \text{mol dm}^{-3}$ juglone stock solution and needs to prepare 500 cm³ of a $1.0 \times 10^{-3}\ \text{mol dm}^{-3}$ juglone solution using a single dilution. You must state the volumes of juglone stock and solvent required.

Approach

Apply $C_1V_1 = C_2V_2$ to find $V_1$ (volume of stock), then subtract from 500 cm³ to find the volume of solvent.

Step-by-Step Reasoning

Substitute into the dilution equation:

So $5\ \text{cm}^3$ of the stock juglone is required. The remaining $500 - 5 = 495\ \text{cm}^3$ must be solvent.

The dilution factor is $0.1 / 1.0 \times 10^{-3} = 100$, so the stock is diluted 100-fold — only a small volume of the concentrate is needed.

Key Takeaways

• $C_1V_1 = C_2V_2$ is the working equation for dilutions.
• The dilution factor is $C_1 / C_2$ and gives the dilution ratio.
• Solvent volume = total volume − stock volume.

Common Mistakes

• Forgetting to subtract the juglone volume from the total volume of solvent (the final volume must be 500 cm³, not 1000 cm³).
• Using the wrong dilution factor — confusing $\times 10$ with $\div 100$.

Things to Be Careful About

• The volumes must sum to 500 cm³ (the stated final volume).
• Note that the stock is 100× more concentrated than the target, so only 1/100 of the final volume is needed as stock.

Background Concept

Percentage error is calculated as:

A typical $10\ \text{cm}^3$ measuring cylinder has an uncertainty of about $\pm 0.1\ \text{cm}^3$ or more; for a $5\ \text{cm}^3$ measurement this gives a substantial percentage error. More precise apparatus (graduated pipettes, burettes, micropipettes, volumetric flasks) has smaller absolute uncertainty, so the percentage error is correspondingly smaller.

Alternatively, percentage error falls as the measured volume rises, so preparing and using a larger volume of stock (e.g. by first preparing an intermediate dilution) reduces percentage error.

Understanding the Question

The student used measuring cylinders to carry out the dilution. You must suggest one improvement that reduces the percentage error.

Approach

Identify the source of large percentage error: measuring a small volume with a measuring cylinder. Fix this by either using more precise apparatus for the small volume, or making the volumes larger so the same absolute error becomes a smaller percentage.

Step-by-Step Reasoning

The $5\ \text{cm}^3$ volume of juglone stock measured with a small measuring cylinder has a relatively high percentage error (cylinder uncertainty may be $\sim \pm 0.1$–$0.5\ \text{cm}^3$, giving 2–10% error). The $495\ \text{cm}^3$ volume measured with a large measuring cylinder has lower percentage error, but is still less precise than a volumetric flask.

Improvements:

• Use a (graduated/serological) pipette, micropipette, syringe or burette to measure the $5\ \text{cm}^3$ juglone more accurately.
• Use a volumetric flask (e.g. $500\ \text{cm}^3$) to make up the final volume precisely.
• Use a larger volume of stock (e.g. take $50\ \text{cm}^3$ of an intermediate $0.01\ \text{mol dm}^{-3}$ dilution) so the same apparatus error becomes a smaller percentage of the measured volume.

Key Takeaways

• Percentage error decreases as measured volume increases.
• More precise apparatus (burette, pipette, volumetric flask) reduces absolute uncertainty.
• For very small volumes, always use apparatus with small absolute uncertainty (micropipette/syringe/burette).

Common Mistakes

• Saying "use a beaker" — beakers have no accurate volume markings and would not improve accuracy.
• Saying "measure more carefully" or "be more accurate" — too vague to score.
• Saying "use more juglone" — this changes the concentration, not the precision.

Things to Be Careful About

• The improvement must be a specific change in technique or apparatus, not a vague statement.

Background Concept

A control is a treatment that differs from the experimental treatment by only one variable — the variable being investigated. By comparing the control with the experimental treatment, any difference in the dependent variable can be confidently attributed to the independent variable rather than to confounding factors.

In this investigation, juglone is dissolved in a mixture of solvents. If you compared the juglone solution with pure water, and the seeds responded differently, you would not know whether the effect was due to juglone or to the solvent mixture. So the control must include everything except the juglone — i.e. the solvent mixture alone.

Understanding the Question

You are asked to:

1. State a suitable control treatment.
2. Explain why a control has been included.

The independent variable is the presence of juglone. The control must therefore contain everything the juglone treatment contains except juglone.

Approach

Identify what is present in the juglone treatment (juglone + solvent mixture + damp soil + cucumber seeds) and design a control that contains the same components minus the juglone.

Step-by-Step Reasoning

• The juglone treatment: damp soil + juglone solution (juglone dissolved in solvent mixture) + cucumber seeds.
• Remove only the juglone: damp soil + solvent mixture (no juglone) + cucumber seeds.
• This is the appropriate control.
• Explanation: without this control, an observed difference between treatments could be due to the solvent rather than juglone. Including the solvent-only control isolates the effect of juglone so any difference between treatments can be confidently attributed to juglone.

Key Takeaways

• A control contains every variable of the experimental treatment except the one being tested.
• The control allows valid comparison and isolates the effect of the independent variable.
• For a chemical dissolved in a solvent, the control must contain the solvent alone.

Common Mistakes

• Saying "water" as the control — this is incorrect because the juglone solution contains a solvent mixture, not pure water. Water would not control for the solvent.
• Saying "seeds without soil" — this removes too many variables.
• Saying "no treatment" or "nothing added" — these are not equivalent to the juglone treatment minus juglone.

Things to Be Careful About

• The control must be identical to the experimental treatment in every way except the presence of juglone.

Background Concept

A valid scientific method must:

• vary only one independent variable (here: presence of juglone),
• measure a dependent variable (here: % germination and seedling growth),
• control all other variables that could affect the outcome,
• use sufficient replication to obtain a reliable mean,
• measure accurately and safely.

To make a fair comparison between two treatments, every other condition (seed type, soil, temperature, light, water) must be identical. Without this, an observed difference cannot be confidently attributed to juglone.

Understanding the Question

You are asked to describe a detailed, reproducible method for investigating the effect of juglone on:

• the percentage of cucumber seeds that germinate, and
• the growth of each cucumber seedling 10 days after the seedling appears above the soil.

The method should be set out logically and be detailed enough for another person to follow, with consideration of safety.

Approach

Organise the method into clear sections, addressing each of the 10 mark-scheme points:

1. Standardising exposure to juglone
2. Same cucumber seeds
3. Constant temperature
4. Constant light intensity
5. Standardising another key variable
6. Reapplying juglone after germination
7. Counting seeds planted AND germinated after a set time
8. Measuring growth at 10 days
9. Using a large number of seeds and calculating a mean
10. Safety (hazard + risk + precaution)

Step-by-Step Reasoning

Treatments — Two identical trays are set up. One receives a measured volume of juglone solution; the other (control) receives the same volume of solvent mixture only. This isolates juglone as the only difference between treatments.

Seeds — The same number, species, size and age of cucumber seeds are planted in each tray. Differences in seed quality would otherwise confound the comparison.

Exposure — The juglone solution (or solvent for the control) is reapplied at regular intervals so that the seedlings continue to be exposed to juglone as they grow.

Temperature — Both trays are placed in the same location at a constant, stated temperature (e.g. 20 °C), e.g. in a thermostatically controlled incubator. Temperature strongly affects both germination rate and seedling growth.

Light — Both trays receive the same light intensity for the same photoperiod (e.g. 12 h day⁻¹ from the same source at the same distance). Light intensity strongly affects photosynthesis and growth.

Water — The same volume of water is added to each tray at the same regular intervals. Water availability is a key variable affecting germination and growth.

Germination measurement — The number of seeds planted in each tray is recorded. After a set, stated time (e.g. 7 days), the number of seeds that have germinated (cotyledons visible above the soil) is counted. Percentage germination = (germinated ÷ planted) × 100.

Growth measurement — 10 days after each seedling first appears above the soil, the shoot length of each seedling is measured from the soil surface to the apex of the tallest leaf using a ruler. This must be done 10 days after the appearance of each seedling, not 10 days after planting, so that all seedlings are compared at the same developmental stage.

Replication — A large number of seeds (e.g. ≥30 per tray) is used so that the mean growth calculated is reliable and not skewed by individual variation.

Safety — The hazards (juglone, solvents), risks (irritant/toxin), and precautions (gloves, mask, ventilation) must all be named.

Key Takeaways

• A good method controls all variables except the one being tested.
• Replication (large sample size) gives a reliable mean.
• Safety requires identifying hazard, risk, and precaution.
• Growth must be compared at the same developmental stage (here: 10 days post-emergence).

Common Mistakes

• Forgetting to water both trays with the same amount (water is a key variable that affects germination).
• Not controlling for light or temperature, which strongly affect germination and growth.
• Saying "measure the growth with a ruler" without specifying what is measured (shoot length) and from where (soil surface to apex).
• Vague safety statements ("be careful") without naming hazard, risk, and precaution.
• Using too few seeds (e.g. 5 per tray) — insufficient replication for a reliable mean.

Things to Be Careful About

• The mark scheme requires ALL THREE of hazard, risk, and precaution for the safety point.
• "Calculate mean" is required — just stating you have multiple seeds is not enough.
• Specify that germination percentage = (number germinated ÷ number planted) × 100.
• Growth should be measured at the same developmental stage for all seedlings (10 days after each emerges, not 10 days after planting).

Background Concept

The Student's $t$-test compares the means of two groups to decide whether any observed difference is statistically significant or could have arisen by chance. It is appropriate when:

• The data is continuous (numerical), not categorical (e.g. counts, percentages).
• Two independent groups are being compared.
• The data in each group is approximately normally distributed.
• The standard deviations of the two groups are roughly similar (variances are approximately equal).
• Sample sizes are reasonably large (although $n = 10$ in each group here is generally acceptable for the $t$-test).

The Mann–Whitney U test would be more appropriate for non-parametric (non-normally distributed) continuous data, and the chi-squared test would be appropriate for categorical data — so these conditions must be checked before choosing a $t$-test.

Understanding the Question

The question states that the sample size (10 seeds per group) is appropriate for the $t$-test. You are asked for two other reasons why the $t$-test is appropriate here.

Approach

Recall the assumptions/requirements of the $t$-test, and identify which apply to this dataset (time taken for germination, two groups of 10 seeds each).

Step-by-Step Reasoning

The data here is:

• Time to germination in days — continuous numerical data ✓
• Two groups (juglone-treated vs untreated) — the $t$-test compares two means ✓
• Germination times are expected to be approximately normally distributed (a property of most biological measurements of time) ✓
• Standard deviations are likely similar between two small, similarly-treated groups of seeds ✓

The two clearest reasons:

1. The data is continuous. Time taken (in days) is a continuous numerical variable, satisfying the $t$-test's requirement for interval/ratio data (as opposed to categorical data, which would require a chi-squared test).
2. The $t$-test compares the means of two groups. The investigation is comparing the mean germination time in the juglone-treated group with the mean germination time in the untreated group — exactly the situation for which the $t$-test was designed.

(Equally acceptable alternatives: the data is expected to be normally distributed; the standard deviations of the two groups are approximately equal.)

Key Takeaways

• The $t$-test requires continuous data, two groups, approximate normality, and similar standard deviations.
• It compares means, not distributions or proportions.
• The chi-squared test would be appropriate for categorical data; Mann–Whitney U for non-normal continuous data.

Common Mistakes

• Restating that the sample size is appropriate — this was given as the first reason in the question.
• Saying the $t$-test "compares data" — too vague; it is the means of the two groups that are compared.
• Saying the $t$-test is appropriate because the data is "accurate" or "reliable" — these are not statistical assumptions.

Things to Be Careful About

• The question asks for reasons beyond the sample size, which has already been given.
• Each reason must relate to a specific assumption or property of the $t$-test.

Background Concept

To use a statistical table to draw a conclusion:

1. Calculate the test statistic (here, $t$).
2. Determine the degrees of freedom. For a two-sample $t$-test, $\text{df} = n_1 + n_2 - 2$.
3. Find the critical value at the chosen significance level (here, $p = 0.05$).
4. If the calculated test statistic exceeds the critical value, reject the null hypothesis (the difference is statistically significant).
5. If the calculated test statistic does not exceed the critical value, accept the null hypothesis (no significant difference).

The null hypothesis is the assumption of "no difference". It is accepted unless the evidence is strong enough to reject it.

Understanding the Question

The calculated value of $t$ is 2.090. Table 1.1 provides critical values of $t$ at different degrees of freedom and probability levels. You must:

1. Calculate the degrees of freedom.
2. Identify the critical value at $p = 0.05$.
3. Compare the calculated $t$ to the critical value.
4. State the conclusion.

Approach

Calculate $\text{df} = n_1 + n_2 - 2 = 10 + 10 - 2 = 18$. Find the critical value at df = 18 and $p = 0.05$ in Table 1.1. Compare the calculated $t$ (2.090) to this critical value and decide whether to accept or reject the null hypothesis.

Step-by-Step Reasoning

• $n_1 = 10$ seeds treated with juglone; $n_2 = 10$ seeds untreated.
• $\text{df} = n_1 + n_2 - 2 = 10 + 10 - 2 = 18$.
• From Table 1.1, at df = 18 and $p = 0.05$, the critical value is 2.101.
• The calculated $t$ is 2.090, which is less than 2.101.
• Because the calculated value does not exceed the critical value at $p = 0.05$, the result is not statistically significant at this level.
• Conclusion: accept the null hypothesis at $p = 0.05$. There is no significant difference between the time taken for seeds to germinate when treated with juglone and the time taken for seeds to germinate when not treated with juglone.

Key Takeaways

• For a two-sample $t$-test, $\text{df} = n_1 + n_2 - 2$.
• Compare the calculated $t$ to the critical $t$ at the chosen df and $p$ value.
• If calculated $t$ < critical $t$: accept the null hypothesis (no significant difference).
• If calculated $t$ > critical $t$: reject the null hypothesis (significant difference).

Common Mistakes

• Using $\text{df} = n - 1$ (e.g. 17 instead of 18) — this is the formula for a one-sample $t$-test, not a two-sample $t$-test.
• Saying "reject" instead of "accept" — easy mistake when the calculated value is close to the critical value.
• Saying the difference is significant because $t = 2.090 > 0$ — the value must be compared to the critical value, not to zero.
• Concluding that "juglone has no effect" — the correct conclusion is "no significant difference" (we can never prove the null hypothesis; we only fail to reject it).

Things to Be Careful About

• Note that $t = 2.090$ is greater than the critical value at $p = 0.10$ (1.734 at df = 18), so there might be a difference at a less stringent significance threshold — but at the standard $p = 0.05$, the result is not significant.
• Use the exact wording from the null hypothesis when stating the conclusion.

Background Concept

Data can be classified by the kind of values they take:

• Nominal data are categories with no intrinsic order — species names, blood groups, eye colours. You can label them but cannot arrange them from smallest to largest in any meaningful way.
• Ordinal data are categories with a natural order but unequal gaps — small / medium / large, exam grades.
• Discrete data are numerical counts that can only take whole-number values — number of children, number of bacteria in a countable plate.
• Continuous data are numerical measurements that can take any value within a range, including fractions and decimals — length, mass, time, concentration.

Recognising the data type matters because it constrains which statistical tests, graph types and summary statistics are appropriate.

Understanding the Question

Fig. 2.1 has two axes. The x-axis lists five distinct bacterial species by name; the y-axis shows a numerical concentration in µmol cm⁻³ with a continuous scale from 0.0 to 6.0. The question asks the candidate to classify each of these two variables.

The part is worth 2 marks — one per correct classification.

Approach

1. For each variable, ask: is it a name/category, or a number?
2. If a name → nominal (or ordinal if there is an order).
3. If a number → ask whether it can take any value (continuous) or only whole numbers (discrete).

Step-by-Step Reasoning

• Species of bacterium — the x-axis shows Yersinia enterocolitica, Salmonella enteritidis, Salmonella typhimurium, Shigella sonnei and Escherichia coli. These are labels with no natural numerical order. → nominal.
• Lowest nitrite concentration — the y-axis is a measured concentration. A value of 0.7 µmol cm⁻³ or 4.3 µmol cm⁻³ is perfectly meaningful, so the underlying variable can take any value within the range. → continuous.

Key Takeaways

• Distinguish categorical (nominal/ordinal) data from numerical (discrete/continuous) data first; this resolves most classification problems.
• Species, colours, sexes and habitats are almost always nominal.
• Concentrations, lengths, masses, times and temperatures are almost always continuous.

Common Mistakes

• Calling species "discrete" because there is a finite number of them. Discrete numerical data are defined by taking whole-number values, not by being finite.
• Calling concentration "discrete" because the bars are drawn at whole- or half-unit intervals on the chart. The underlying variable can take any value; the chart simply chooses a few representative values to display.

Things to be careful about

• Use the exact words nominal and continuous. Mark schemes rarely accept paraphrases such as "categorical" or "quantitative".

Background Concept

Percentage change expresses one value as a percentage of another. The standard formula is

When two values A and B are compared without a clear "original", the calculation can be carried out using either as the denominator. Both answers are routinely accepted by examiners — using the smaller value as the denominator produces a larger percentage, using the larger value as the denominator produces a smaller one. (The strict "percentage difference" formula uses the mean of the two values as the denominator, but the mark scheme here only credits the two single-denominator versions.)

Understanding the Question

The candidate is given Fig. 2.1, a bar chart of nitrite concentrations for five bacterial species, and is asked to compare the two Salmonella species shown. The two relevant bars are:

• Salmonella enteritidis ≈ 0.7 µmol cm⁻³
• Salmonella typhimurium ≈ 5.0 µmol cm⁻³

The candidate must read these off the chart, set up a calculation, show working and give a percentage.

The part is worth 3 marks: one for correct readings, one for correct working, one for the correct final value.

Approach

1. Read each bar height accurately to one decimal place. Gridlines on the y-axis are at 0.5 µmol cm⁻³ intervals, so values between must be estimated.
2. Choose one value as the reference (denominator) and substitute into the percentage-change formula.
3. Quote the answer to 2–3 significant figures.

Step-by-Step Reasoning

Difference between the two values:

Using S. enteritidis (the smaller value) as the denominator:

Using S. typhimurium (the larger value) as the denominator:

The mark scheme accepts both 614% and 86%.

Key Takeaways

• A percentage-difference calculation requires a reference value; when the question does not specify which to use, both options are usually credited.
• Always show your working, including the values read from the graph with their units.
• Match significant figures to the precision of the readings — here, 2 significant figures (614% or 86%) is appropriate.

Common Mistakes

• Misreading S. enteritidis as 1.0 µmol cm⁻³ (the bar is clearly below 1.0). With this misread the answer becomes 400%.
• Forgetting to include the units in the working even though only a percentage is asked for.
• Reporting only one answer without showing the working, then having no credit if the answer happens to be wrong.

Things to be careful about

• The question explicitly says "show your working" — so the read-off values, the subtraction and the division must all be visible.
• Inside LaTeX math the percent character must be escaped as \% so it is not interpreted as a comment marker.
• If you round, give 2–3 significant figures; 614% is correct, 614.2857…% is excessive.

Background Concept

A bar chart encodes a numerical value for each category as the height of a bar. To compare categories, you read off the heights. The most affected category is the one whose bar reaches the extreme end of the scale that corresponds to greater effect; here, the lowest nitrite concentration needed to stop growth means the species is most affected (because even a tiny amount of nitrite inhibits it). The least affected species needs the highest nitrite concentration.

Understanding the Question

The candidate is asked to identify, from Fig. 2.1:

• the species most affected by nitrite ions — i.e. the one stopped from growing by the lowest nitrite concentration;
• the species least affected — i.e. the one needing the highest nitrite concentration.

The part is worth 1 mark, awarded for both answers together.

Approach

1. Read the bar heights from Fig. 2.1.
2. Identify the smallest bar (most affected) and the largest bar (least affected).

Step-by-Step Reasoning

Bar heights:

• Yersinia enterocolitica ≈ 0.5 µmol cm⁻³ ← shortest bar
• Salmonella enteritidis ≈ 0.7 µmol cm⁻³
• Salmonella typhimurium ≈ 5.0 µmol cm⁻³ ← tallest bar
• Shigella sonnei ≈ 3.0 µmol cm⁻³
• Escherichia coli ≈ 1.0 µmol cm⁻³

Therefore:

• most affected = Yersinia enterocolitica (smallest concentration)
• least affected = Salmonella typhimurium (largest concentration)

Key Takeaways

• "Most affected" means most sensitive — the species inhibited at the lowest concentration of the agent.
• "Least affected" means most resistant — the species needing the highest concentration.
• Always cross-check bar heights against the y-axis gridlines before naming a species.

Common Mistakes

• Picking the longest bar as "most affected" — this is a common reversal; it actually means least affected.
• Naming the wrong Salmonella species — S. enteritidis is the sensitive one, S. typhimurium the resistant one.

Things to be careful about

• Italicise the binomial names (Yersinia enterocolitica, Salmonella typhimurium). Cambridge mark schemes expect correct scientific nomenclature.