Scientific Questions, Hypotheses, and Experimental Variables
A scientific investigation starts with a testable question and a hypothesis, and it is designed around three kinds of variables: the independent variable that you change, the dependent variable that you measure and the controlled variables that you keep the same. In brief: in an experiment on how temperature affects the rate of enzyme activity, temperature is the independent variable, the rate of reaction is the dependent variable, and pH and enzyme concentration are controlled. A control group is a comparison group that does not receive the treatment.
Good experimental design makes the difference between a result that can be trusted and one that cannot. This guide explains how to write scientific questions and hypotheses, how to identify variables, what a control group does, how to plan a fair test with a worked example and which mistakes to avoid.
What is a scientific question?
A scientific question can be answered by observation or measurement. It is specific and it names what will be changed and what will be measured. “Why is nature beautiful?” cannot be tested, but “How does the concentration of salt affect the germination of cress seeds?” can be.
Good questions often begin with “how”, “what effect” or “does”. They arise from observations, for example noticing that plants on a windowsill lean toward the light. A narrow question is easier to investigate than a broad one.
What is a hypothesis?
A hypothesis is a proposed, testable explanation or prediction. It is often written as an “if … then … because …” statement: “If the concentration of salt in the water is increased, then fewer cress seeds will germinate, because salt water draws water out of the seeds by osmosis.”
A hypothesis must be falsifiable, which means evidence could show it to be wrong. Scientists do not prove a hypothesis absolutely. Results either support it or do not, and repeated support increases confidence. A related idea is the null hypothesis, which states that there is no effect or no difference, and statistical tests are used to decide whether the data allow it to be rejected.
What are independent, dependent and controlled variables?
A variable is a factor that can change in an investigation.
- Independent variable: the factor that you deliberately change. It is plotted on the x-axis. Only one should be changed at a time in a fair test.
- Dependent variable: the factor that you measure to see the effect. It depends on the independent variable and is plotted on the y-axis.
- Controlled variables: factors that you keep constant, so that they cannot affect the result. Examples are temperature, volume of liquid, time, light and the type and size of the organism.
A memory aid is to ask: “What do I change? What do I measure? What do I keep the same?” The independent variable also needs a range and an interval. For example, salt concentrations of 0, 1, 2, 3 and 4 percent give five points for a graph.
What is a control group?
A control group is a set of samples treated in the same way as the experimental groups, except that the independent variable is absent or set at a standard level. It acts as a comparison. If seeds germinate well in pure water, this shows that the seeds are viable and the setup works, so poorer germination in salt water can be attributed to the salt.
Be careful with the terms. “Control group” refers to the comparison sample. “Controlled variables” are the factors kept constant. A placebo is a control in medical studies, where the comparison group receives a harmless substance.
Variables compared
| Type | What you do | Where on a graph | Example |
|---|---|---|---|
| Independent | Change it | x-axis | Salt concentration |
| Dependent | Measure it | y-axis | Percentage germination |
| Controlled | Keep it constant | Not plotted | Temperature, seed type, volume |
| Control group | Leave untreated | A reference point | Seeds in pure water |
How do you design a biological experiment?
- State the question and write a hypothesis.
- Identify the independent, dependent and controlled variables, and decide how each will be measured.
- Choose the range and number of values for the independent variable.
- Decide on a control group.
- Plan repeats. Using several samples at each value, usually at least three, allows you to calculate a mean and spot unusual results.
- List materials and write a method that someone else could follow.
- Consider safety and ethics, especially when living organisms are involved.
- Plan how the data will be recorded and displayed.
Living things vary naturally, so a bigger sample gives more reliable results. A pilot test with a small number of samples can reveal problems with the method before the full experiment.
Worked example: light and plant growth
A student wants to know whether the amount of light affects the growth of bean seedlings.
- Question: How does the number of hours of light per day affect the dry mass of bean seedlings after 14 days?
- Hypothesis: If bean seedlings receive more hours of light per day, then their dry mass after 14 days will be greater, because they can photosynthesize for longer and make more sugars.
- Independent variable: hours of light per day: 0, 6, 12 and 18.
- Dependent variable: dry mass of the seedling in grams at day 14, found by drying the seedlings in an oven until the mass stops changing. Dry mass is a better measure than height, because seedlings in darkness grow tall and pale as they stretch toward light.
- Controlled variables: bean variety, seed age, pot size, soil type, volume of water per day, temperature and light intensity (same lamp at the same distance).
- Control group: the 12-hour group may be taken as the standard daylight comparison. The 0-hour group shows growth in darkness.
- Repeats: five seedlings for each light condition, so 20 seedlings in total.
Suppose the mean dry masses are 0.20 g (0 hours), 0.45 g (6 hours), 0.80 g (12 hours) and 0.95 g (18 hours). Mass increases with the hours of light, which supports the hypothesis, and the gain from 12 to 18 hours (0.15 g) is smaller than the gain from 6 to 12 hours (0.35 g). This suggests that another factor, such as water or minerals, may begin to limit growth. The result would still need repeating, and a graph with hours on the x-axis and mean dry mass on the y-axis would show the trend clearly.
How did experimental method develop?
Ibn al-Haytham, working in the eleventh century, stressed testing ideas by experiment in his work on optics. In the seventeenth century Francis Bacon and Galileo promoted systematic observation and experiment. In biology, Francesco Redi’s 1668 experiment with jars of meat, some covered and some open, is a famous early controlled experiment, in which the covered jars served as a control group. Karl Popper argued in the twentieth century that science progresses through hypotheses that can be falsified.
Which mistakes are common?
Mistake 1: Changing more than one variable
If light and water both change, you cannot tell which one caused the effect. Change only one independent variable at a time.
Mistake 2: Mixing up independent and dependent variables
The independent variable is what you change. The dependent variable is what you measure in response.
Mistake 3: Writing an untestable hypothesis
A hypothesis that cannot be tested by measurement cannot be supported or rejected. Name the variables and the expected direction of the effect.
Mistake 4: Confusing control group with controlled variables
The control group is a comparison sample. Controlled variables are the conditions that you hold constant.