Practical Skills

Unit 6 (WBI16, "Practical Skills in Biology II") is a written paper of 1 h 20 min worth 50 marks. Like Unit 3, it contains no multiple-choice questions — every item is open-response or calculation — calculators are allowed, and at least 5 marks test Level-2-or-above mathematics. It assesses AO3 (the design, analysis and evaluation of experiments) most heavily, with some AO2.

How Unit 6 differs from Unit 3. The skills are identical — (1) Planning, (2) Implementation & measurements, (3) Processing results — but Unit 6 is set on IA2 content (Topics 5–8: energy flow, microbiology and immunity, respiration, coordination and gene technology) and is explicitly synoptic: a single question can require knowledge from any of the six units. The biggest practical difference is the maths, which is more advanced than Unit 3:

• Logarithms and exponential growth — bacterial number N = N₀ × 2ⁿ, log scales and log graph paper, reading the exponential (log) phase of a growth curve.
• Simpson's index of diversity D = N(N−1) ÷ Σn(n−1) from quadrat data (synoptic with Unit 2 biodiversity).
• Standard deviation and error bars — calculate/interpret SD and judge significance from whether error bars overlap.
• Q10, the inverse-square law for light intensity (∝ 1/d²), percentage uncertainty/change, and gradient/tangent rates.

The questions are built on Core Practicals 10–18, but, as in Unit 3, they are often presented in an unfamiliar context — you apply the same skills to a novel scenario. The plan checklist is unchanged: "I-D-C-A-R-H" — Independent variable, Dependent variable, Control variables (named + how controlled), Apparatus (with resolution), Repeats and range, Health & safety (and, for living organisms, ethics).

Planning questions (commonly 4–8 marks) are the highest-value items on the paper, and on Unit 6 they are set on IA2/synoptic contexts (e.g. rate of photosynthesis, microbial growth, heart rate in Daphnia, ventilation in exercise).

Variables.

• The independent variable (IV) is the one factor you deliberately change (e.g. light intensity, temperature, caffeine concentration, exercise level). State its range and interval.
• The dependent variable (DV) is what you measure in response (e.g. volume/number of O₂ bubbles per minute, heart rate in beats per minute, breathing rate).
• Control variables must be NAMED with the method of control stated. "Keep conditions the same" scores nothing; "keep the temperature constant using a thermostatically controlled water bath at 25 °C" is creditworthy. In CP10, controlling temperature is essential because changing the light source can also change temperature.

Apparatus and resolution. Choose apparatus whose resolution is fine enough and justify it — e.g. use a gas syringe or count bubbles to a timed schedule for O₂ evolution; use a quadrat of stated size and a transect line for CP11; use a data logger with an oxygen, light or temperature probe (CP18) for objective, frequent, automatically-logged readings.

Range, interval and repeats. Use at least five values of the IV across a sensible range and repeat each at least three times, taking a mean. Repeats let you identify anomalies and make data more reliable. For ecological sampling (CP11), use enough quadrats placed by random sampling (random coordinates) — or systematic sampling along a transect where you are studying a gradient.

Ethics and safety. For living organisms, address ethics: handle Daphnia gently, do not harm them, keep them in pond water and return them afterwards (CP16); use the lowest effective concentration of caffeine. State a specific named hazard + matching precaution: microbial cultures (CP13) → aseptic technique and incubate at ≤25 °C; scalpel in a heart dissection (CP14) → cut away from the body on a tile; near a pond → risk of contaminated water, wash hands.

Unit 6 maths goes beyond Unit 3. Master these and always present formula → substitution → answer with units and sensible significant figures.

Exponential bacterial growth and logarithms (CP13). In the exponential (log) phase, bacteria divide by binary fission at a constant rate, so the number is:

$N = N_0 \times 2^{n}$

where N₀ = the starting number, N = the number after growth, and n = the NUMBER OF DIVISIONS. Critically, n = time ÷ mean division (generation) time — not the time itself. If a culture starts at 500 cells with a 20-minute division time, then in 2 hours (120 min) n = 120 ÷ 20 = 6, so N = 500 × 2⁶ = 500 × 64 = 32 000 cells. Because the numbers rise so fast, growth curves are plotted on a logarithmic scale (log₁₀ of cell number), on which the exponential phase becomes a straight line — making it easy to read off the constant growth rate.

Standard deviation and error bars. SD measures the spread of repeat values about the mean: a small SD = repeats close together (precise); a large SD = widely spread. Plotted as error bars of ±1 SD on a bar chart or graph, they let you judge significance: if the error bars of two means do NOT overlap, the difference is likely to be significant (real); if they DO overlap, the difference may not be significant (could be due to chance).

Simpson's index of diversity (CP11, synoptic with Unit 2). From quadrat counts of each species:

$D = \dfrac{N(N-1)}{\sum n(n-1)}$

where N = total number of organisms of all species and n = number of organisms of each individual species. A higher D = greater biodiversity — a more diverse community, generally more stable and better able to resist environmental change.

Q10 (CP12, enzymes — synoptic with respiration). The temperature coefficient is:

$Q_{10} = \dfrac{\text{rate at } (T+10)}{\text{rate at } T}$

A Q10 of about 2 means the rate doubles for every 10 °C rise (typical of enzyme-controlled reactions over their working range).

Inverse-square law for light (CP10). Light intensity is inversely proportional to the square of the distance from the source: light intensity ∝ 1/d². Doubling the distance quarters the intensity. So in CP10 you can vary light intensity by changing the lamp distance d and use a relative intensity of 1/d² as the independent variable (or measure intensity directly with a light meter / data logger).

Plotting conventions (every one is examined). Put the independent variable on the x-axis and the dependent variable on the y-axis; label both axes with the quantity and unit; choose a sensible scale so the points fill at least half the grid; plot points accurately; draw a single smooth line of best fit (or straight line if linear) — never "dot-to-dot".

Log axes. When values span several orders of magnitude (e.g. bacterial number 10² → 10⁷), plot the log₁₀ of the value (or use log graph paper). On a log scale, equal distances represent equal MULTIPLES (×10), not equal additions, and the exponential phase becomes a straight line. When reading a log axis, beware the unequal gridlines within each decade — a frequent source of misreading.

Gradient (straight line). Read the gradient with a LARGE triangle spanning most of the line: gradient = Δy ÷ Δx, and you must give the units (units of y ÷ units of x), e.g. cm³ s⁻¹ or cells h⁻¹.

Tangent and rate (curve). For a curve (e.g. O₂ volume against time in CP10, or a growth curve before log-transforming), the rate at a point is the gradient of the tangent at that point; the initial rate is the gradient of the tangent at t = 0. Read two well-separated points on the tangent and compute Δy/Δx.

Evaluation questions ask you to judge the data/method and how to improve them.

Accuracy vs precision vs reliability vs validity. Accuracy = closeness to the true value (improved by calibration). Precision = closeness of repeats to each other (small SD; finer apparatus). Reliability = consistency on repeating (more repeats + mean). Validity = a fair test measuring what it claims, with all variables controlled and a suitable control.

Random vs systematic error. Random error scatters readings both sides of the true value (e.g. counting bubbles by eye, slight differences between organisms) — reduced by more repeats and a mean, shown by a large SD. Systematic error shifts every reading the same way (e.g. a data logger probe not zeroed, a stopwatch started late, parallax read from one side) — not reduced by repeating; corrected by recalibrating / zeroing / removing the cause.

Percentage uncertainty. The absolute uncertainty of a single reading is ± half the smallest division (analogue) or ± the smallest division (digital); for a measurement from two readings it doubles. Then:

$\text{percentage uncertainty} = \dfrac{\text{absolute uncertainty}}{\text{reading}} \times 100$

A larger reading has a smaller percentage uncertainty; combine uncertainties by adding the percentages.

Data logger vs manual measurement (CP18). A data logger with a probe (O₂, CO₂, temperature, light, pH) is often better than manual measurement because it:

• takes readings automatically and very frequently, capturing rapid changes you would miss by hand;
• removes subjective/human reaction-time error (no judging colour by eye, no manually counting bubbles);
• gives readings of high, consistent resolution and stores them digitally, reducing random error.

But a data logger must be calibrated before use (against a known standard), or it introduces a systematic error, and it can drift. Manual measurement may still be needed where no probe exists (e.g. counting cells in a quadrat). When asked to evaluate, state the specific advantage in this context (e.g. "a data logger records O₂ concentration every second, capturing the initial rate accurately") rather than a generic "it's more accurate".

Suggesting improvements. Improvements must be specific and creditworthy: "use a data logger with an O₂ probe to record continuously and remove the error of counting bubbles by eye"; "calibrate the probe with a known standard before use to remove systematic error"; "take more quadrats placed by random sampling to improve reliability and reduce bias"; "control temperature with a water bath at a stated value". Not creditworthy: "do it more carefully", "be more accurate".

Unit 6 questions are built on these nine Core Practicals (and may reuse CP1–9). Know the technique, what is measured, and the maths/skill each tends to assess.

CP	Practical	Technique / measurement	Likely skill examined
CP10	Rate of photosynthesis	O₂ evolution from pondweed (Elodea); vary light intensity/wavelength/temp/CO₂	Counting bubbles / gas volume; inverse-square law (1/d²); rate from a tangent; control temperature
CP11	Ecology of a habitat	Quadrats & transects + abiotic factors; % cover, counts	Random vs systematic sampling; mean, SD; Simpson's index of diversity; representative sample
CP12	Temperature & enzyme activity	Amylase/starch with iodine colour; rate vs temperature	Rate (1/time); Q10; control variables; line of best fit
CP13	Serial dilutions & growth curves	Dilution factor; viable count (cfu); microbial growth curve	Dilution factor; N = N₀ × 2ⁿ (n = time ÷ division time); log scales; exponential phase
CP14	Mammalian heart dissection	Dissect a heart; relate structure to function	Identifying chambers/vessels; structure→function; evaluating a dissection
CP15	Factors affecting heart rate	Measure heart rate under different conditions	Mean heart rate; variables; control; reliability
CP16	Chemicals on Daphnia heart rate	Effect of caffeine; count heartbeats per minute	Variables; ethics (handle gently, return); count under microscope; mean
CP17	Effect of exercise on ventilation	Tidal volume, breathing rate before/after exercise	Ventilation rate = tidal volume × breathing rate; mean; % change
CP18	Calibrate & use a data logger	Calibrate a probe; measure environmental factors	Calibration; systematic error; data logger vs manual; resolution

When a question presents an unfamiliar scenario (e.g. a new antibiotic's effect on microbial growth, or a different stimulant on heart rate), match it to the closest Core Practical and apply the same skills — variables, control, the right measurement, the right maths and its uncertainty.