Scientific Method – Dr Tracey Classens

The Scientific Method

Step by Step

🔬 Why This Topic Gives You Free Marks

Every IEB and CAPS practical, PAT, and data-response question is built on the scientific method. Whether you're being asked to identify the independent variable, write a hypothesis, or explain a control — you're being tested on these exact steps. Learn them once, apply them everywhere.

Step 1

Observation / Identifying the Problem

A scientist notices something in the natural world that raises a question. The observation must be objective — based on what you can detect with your senses or instruments, not on opinion or interpretation.

📌 Example: "I notice that plants near the window grow taller than those further away."

Step 2

Research / Background Information

The scientist gathers existing information about the topic — from textbooks, journals, previous studies. This informs the hypothesis and helps design a valid experiment.

📌 Example: "Research shows that light is needed for photosynthesis, which produces glucose used in plant growth."

Step 3

Formulating a Hypothesis

A hypothesis is a testable, falsifiable prediction of the expected outcome, written as an "If…then…because" statement or as a relationship between variables. It must be specific and based on prior knowledge.

📌 Example: "If light intensity increases, then the rate of plant growth will increase, because more light energy is available for photosynthesis."

Step 4

Designing the Experiment

Plan a fair test that will collect reliable data to test the hypothesis. This includes identifying variables (independent, dependent, controlled), choosing apparatus, and determining the method. A control group must be included.

📌 Example: "Grow 5 identical plants at each of 4 different light intensities (0, 25, 50, 100% of maximum) for 4 weeks. Keep soil, water, and temperature constant."

Step 5

Collecting Data / Results

Conduct the experiment and record observations and measurements systematically in a results table. Data must be recorded accurately, with units, and repeated to improve reliability. Distinguish between quantitative (numerical) and qualitative (descriptive) data.

📌 Example: "Measure and record the height of each plant in cm every 7 days. Calculate the mean height for each light intensity group."

Step 6

Analysing Data / Drawing Graphs

Organise data into graphs or tables to reveal patterns and trends. Choose the correct graph type: line graph (continuous data / trends over time), bar graph (discrete categories), pie chart (proportions of a whole). Label axes with units. Include a title.

📌 Example: "Draw a line graph of mean plant height (y-axis, cm) vs light intensity (x-axis, %). Draw a line of best fit."

Step 7

Drawing Conclusions

State whether the results support or reject the hypothesis, and explain why using your data. A conclusion must refer directly to the hypothesis and quote specific results as evidence. Do NOT introduce new information in the conclusion.

📌 Example: "The results support the hypothesis. Plants at 100% light intensity reached a mean height of 18.4 cm compared to 6.1 cm at 25% light, showing that increased light intensity increases plant growth rate."

Step 8

Evaluation & Communication

Evaluate the experiment: identify sources of error, assess reliability (were results repeatable?) and validity (did the experiment test what it was meant to?). Suggest improvements. Communicate findings — in a scientific report or peer-reviewed publication.

📌 Example: "A source of error was unequal distribution of artificial light across the growth chamber. Future experiments should use individually controlled light chambers for each group."

⚠️ Conclusion vs Discussion — Know the Difference

Conclusion: brief, directly links results to hypothesis — accept or reject with evidence.
Discussion: longer; explains WHY the results occurred using biological knowledge; compares to existing literature; acknowledges limitations.
IEB PAT questions often ask for both — losing marks here is avoidable.

Writing a Hypothesis

Formulation

💡 The Most Commonly Dropped Marks in Practicals

Students lose marks on the hypothesis more than almost any other section — usually because they state it as a question, make it too vague, or forget the "because" component. There is a formula. Use it every time.

📐 THE HYPOTHESIS FORMULA

"If [independent variable] increases / decreases / changes,
then [dependent variable] will increase / decrease / change,
because [biological reason based on prior knowledge]."

✅ Example — Photosynthesis experiment

"If the light intensity increases, then the rate of photosynthesis (measured by O₂ production) will increase, because more light energy is available for the light-dependent reactions in the chloroplast."

✅ A Good Hypothesis Must Be:

Testable — can be tested by a practical experiment
Falsifiable — it is possible to prove it wrong
Specific — clearly states which variables are involved
Based on prior knowledge — the "because" must be scientifically valid
A statement, not a question — never "Does light affect photosynthesis?"
Predicts a direction — increase, decrease, no effect

❌ Common Hypothesis Mistakes:

Written as a question: "Does temperature affect enzyme activity?" ✗
Too vague: "Temperature affects enzymes" — no direction or reason ✗
No "because": "If temperature increases, then reaction rate increases" — loses the reasoning mark ✗
Circular reasoning: "…because higher temperature increases the reaction" — just restates the prediction ✗
Untestable: "Plants grow better when they are happy" ✗

Scenario	Weak hypothesis ✗	Correct hypothesis ✓
Effect of pH on enzyme activity	Does pH affect enzymes?	If the pH moves away from the optimum (pH 7), then the rate of enzyme activity will decrease, because extreme pH denatures the enzyme's active site, preventing substrate binding.
Effect of exercise on heart rate	Exercise increases heart rate.	If the intensity of exercise increases, then heart rate will increase, because muscles require more oxygen for aerobic respiration, so the heart must beat faster to deliver oxygenated blood.
Effect of fertiliser on plant growth	Fertiliser is good for plants.	If the concentration of nitrogen fertiliser increases, then plant height will increase, because nitrogen is required for amino acid and protein synthesis, supporting cell growth and division.

📌 Null Hypothesis

IEB sometimes asks for a null hypothesis — this states that there is NO relationship between the variables.
Formula: "There is no significant relationship between [IV] and [DV]."
Example: "There is no significant relationship between light intensity and the rate of photosynthesis."
The null hypothesis is rejected if your results show a clear pattern; it is accepted if results show no effect.

Variables

IV · DV · CV

⚗️ The Three Variables Every Experiment Has

Every fair experiment involves exactly one independent variable — the one you deliberately change. Everything else that could affect the result must be controlled. The dependent variable is what you measure. Get these wrong and your entire experiment is invalid.

🎛️

Independent Variable (IV)

The variable you deliberately change or manipulate. There is only ONE IV per experiment. All other conditions must be kept the same.

Ask yourself: "What am I changing in this experiment?"

IV = what you change

📏

Dependent Variable (DV)

The variable you measure or observe — it depends on (responds to) the independent variable. Must be measurable and recorded with units.

Ask yourself: "What am I measuring to get my results?"

DV = what you measure

🔒

Controlled Variables (CV)

All variables that are kept constant throughout the experiment to ensure a fair test. There are always multiple controlled variables. Changing any one of them would make results unreliable.

Ask yourself: "What must I keep the same?"

CV = what you keep constant

Experiment	Independent Variable	Dependent Variable	Controlled Variables (examples)
Effect of temperature on enzyme activity	Temperature (°C)	Rate of reaction (e.g. time for colour change, volume of gas produced)	pH, enzyme concentration, substrate concentration, volume of solution
Effect of light intensity on photosynthesis	Light intensity (lux or distance from light)	Rate of O₂ production (bubbles/min) or CO₂ absorbed	Temperature, CO₂ concentration, type of plant, volume of water
Effect of exercise on pulse rate	Type/intensity of exercise	Pulse rate (beats per minute)	Age, health, resting time between measurements, time of measurement
Effect of fertiliser on plant growth	Fertiliser concentration (g/L)	Plant height (cm) after set time period	Light, water volume, soil type, temperature, plant species, pot size

🧪 Control Group vs Experimental Group

Experimental group: receives the independent variable treatment (e.g. different temperatures, different concentrations)

Control group: receives NO treatment or the baseline/standard condition (e.g. 0°C, 0 g/L fertiliser, no exercise). It allows comparison and confirms that any change in the DV is caused by the IV — not some other factor.

Without a control group, you cannot prove cause and effect.

📐 Reliability vs Validity

Reliability: how consistent the results are when the experiment is repeated. Improved by repeating the experiment multiple times and calculating a mean. Outliers/anomalous results should be excluded from the mean.

Validity: whether the experiment actually measures what it claims to measure. An experiment is valid if there is only one IV and all other variables are controlled. An invalid experiment gives misleading results even if it is reliable.

⚠️ The Most Common Variable Mistake

Students often confuse the independent and dependent variables — especially when writing the hypothesis. A quick test: the IV is plotted on the x-axis of your graph; the DV is on the y-axis. If your graph axes are wrong, your variables are wrong.

Data, Results & Graphs

Recording & Displaying

📊 Collecting and Displaying Data Correctly

Many students collect perfectly good results but lose marks because their table is missing units, their graph axes are unlabelled, or they chose the wrong graph type. Data presentation is a skill — and a learnable one.

How to Draw a Perfect Results Table

IEB markers check for all of these — each is worth a mark

Requirement	What to do	Common mistake
Title	State what the table shows — include both variables	No title, or too vague ("Results")
Column headings	Name of variable, with units in brackets — e.g. "Temperature (°C)"	No units, or units in the data cells instead
IV column	First column (left); list the values you set	Putting DV first
DV column(s)	One column per measurement; include a mean column if repeated	No mean calculated; anomalous result included in mean
Decimal consistency	All values in a column to the same number of decimal places	Mixing 2.0, 2.50, 3 in same column
Borders	Full table with grid lines	No lines; data presented as a list

Graph Type	When to use it	IEB example
Line graph	IV is continuous (e.g. temperature, time, concentration); shows trends and rate of change	Rate of photosynthesis vs light intensity; heart rate vs time during exercise
Bar graph	IV is discrete/categorical (e.g. species, gender, treatment type); comparing separate groups	Mean height of plants grown in different soil types; number of organisms per habitat
Histogram	Showing frequency distribution of a continuous variable; bars touch each other	Distribution of body masses in a population; distribution of petal lengths
Pie chart	Showing proportions/percentages of a whole; parts must add up to 100%	Proportion of biomes in South Africa; percentage composition of blood

📌 Graph Drawing Checklist

✅ Title: "Graph showing [DV] vs [IV]"
✅ X-axis = IV with units
✅ Y-axis = DV with units
✅ Even, consistent scale on both axes
✅ All points plotted accurately (use a cross ✕)
✅ Line of best fit OR join the dots (as instructed)
✅ Key / legend if more than one data set

📐 Quantitative vs Qualitative Data

Quantitative data: numerical measurements with units. More precise, easier to analyse statistically.

Height in cm, mass in g, temperature in °C, pulse in bpm

Qualitative data: descriptive observations — colour, texture, behaviour, presence/absence.

Colour change from blue-black to amber; plant wilted; organism was present

⚠️ Anomalous Results — Handle With Care

An anomalous result (outlier) is a data point that does not fit the general trend. In your results table: circle it and exclude it from your mean calculation. In your evaluation: identify it and suggest a reason (e.g. contamination, measurement error, equipment malfunction). Never just delete or ignore it — acknowledge it.

Designing a Valid Experiment

Fair Test

🧪 What Makes an Experiment "Fair"?

An IEB Paper II case study will often give you a flawed experiment and ask you to identify the problem and suggest improvements. Or it will ask you to design an experiment from scratch. These questions follow a predictable pattern — and they reward structured answers.

The Anatomy of a Well-Designed Experiment

Every valid experiment must include all of these elements

Element	What IEB expects	Marks available for
Aim	"To investigate the effect of [IV] on [DV]"	1 mark — must name both variables
Hypothesis	If…then…because format, with biological reason	2–3 marks — direction + reason
Apparatus list	Specific items with quantities; e.g. "5 × 250 mL beakers"	1–2 marks
Method / procedure	Numbered steps; clear enough for someone else to repeat; state how many times	3–5 marks — each step = mark
Variables	State IV, DV, and at least 3 CVs explicitly	3+ marks
Control	Describe the control group clearly	1–2 marks
Results table	Correct headings, units, space for repeats and mean	2–3 marks
Conclusion	Accept/reject hypothesis with data evidence	2 marks

📌 The Aim Formula

Always write the aim as: "To investigate the effect of [independent variable] on [dependent variable]."

✅ "To investigate the effect of temperature on the rate of enzyme activity."
❌ "To see if enzymes work better at some temperatures."

Exam Tips & Memo Answers

IEB Style

📝 Scientific Method Questions Are Predictable

IEB examiners use the same question types year after year. Once you know the template, these become some of the most reliable mark-scorers in the paper. Here are the most common question types with full memo answers.

❓ Formulate a hypothesis for an experiment investigating the effect of temperature on the rate of transpiration in plants. (3 marks)

✅ Memo Answer

✓If temperature increases,

✓then the rate of transpiration will increase,

✓because higher temperatures increase the kinetic energy of water molecules, increasing evaporation from the leaf surface, and also lower the relative humidity of the air around the stomata, increasing the water vapour concentration gradient.

❓ A learner set up an experiment to test the effect of light colour on photosynthesis rate, but used plants of different sizes. Identify TWO problems with this experiment and suggest how each could be improved. (4 marks)

✅ Memo Answer

✓Problem 1: Plants are different sizes — larger plants have more leaf surface area and more chloroplasts, so they will photosynthesise at a higher rate regardless of light colour.

✓Improvement 1: Use plants of the same species, age, size/mass and number of leaves in all experimental groups.

✓Problem 2: Only one controlled variable problem is identified — there is no mention of whether light intensity was kept constant across the different colours. Different coloured filters may transmit different intensities of light.

✓Improvement 2: Ensure the light intensity (lux) reaching each plant is equal across all colour conditions, measured with a light meter.

❓ After an experiment, a learner states: "My results prove that exercise increases heart rate." Explain why the word "prove" is scientifically incorrect in this context. (2 marks)

✅ Memo Answer

✓Scientific experiments can only support or reject a hypothesis — they cannot "prove" it, because science is based on falsifiability. Any conclusion is only valid under the specific conditions tested.

✓The results may support the hypothesis, but different conditions (different subjects, different exercise type, different measurement method) could yield different results. The correct statement would be: "The results support the hypothesis that exercise increases heart rate."

⚠️ Language That Loses Marks

❌ "proves" → ✅ "supports" or "rejects"
❌ "shows that" → ✅ "suggests that"
❌ "the experiment was successful" → meaningless
❌ "as expected" → no data cited
❌ "the results were accurate" → accuracy ≠ conclusion
❌ hypothesis as a question → must be a statement

📌 Quick Recall

✅ Aim = "To investigate effect of IV on DV"
✅ Hypothesis = If…then…because
✅ IV = x-axis; DV = y-axis
✅ Control group = no treatment / baseline
✅ Reliability = repeats + mean
✅ Validity = only 1 IV; all CVs controlled
✅ Conclusion = accept/reject + specific data
✅ Anomalous result = circle, exclude from mean

Test Yourself

Quiz

🎯 Scientific Method Quiz

These questions are written in the style of IEB Paper I and Paper II data-response questions. Select your answer — if incorrect, the correct answer highlights immediately with a full explanation.

A learner wants to test whether the amount of water given to a plant affects its height. She grows 10 plants — 5 receive 100 mL of water per day and 5 receive 200 mL per day. What is the independent variable in this experiment?

A learner writes the following hypothesis: "Does increasing CO₂ concentration affect the rate of photosynthesis?"

Identify TWO problems with this hypothesis and rewrite it correctly.

An experiment tests the effect of antibiotic concentration on bacterial growth. Three concentrations are tested: 0 mg/mL, 5 mg/mL, and 10 mg/mL. The 0 mg/mL group is included in every trial.

What is the purpose of the 0 mg/mL group, and what is it called?

Results from an experiment on enzyme activity at different temperatures (°C): 10°C → 2.1 cm³/min; 20°C → 4.3 cm³/min; 30°C → 8.2 cm³/min; 40°C → 4.1 cm³/min; 50°C → 0.3 cm³/min. The result at 30°C was recorded as 8.2 in one trial and 1.1 in another (mean used = 4.65).

The learner's conclusion states: "The results prove that enzymes are destroyed at 50°C." Identify TWO errors in this conclusion. (4 marks)

What is the difference between the reliability and validity of an experiment?

Q6 — Application

A scientist investigates whether a new drug lowers blood pressure. She gives 200 patients the drug and 200 patients a sugar pill (placebo). Neither the patients nor the doctors measuring blood pressure know who received which pill. Blood pressure is measured before and after treatment.

This experimental design is called a double-blind trial. Explain why BOTH the patients AND the doctors measuring results are kept unaware of who received the drug. (4 marks)