Nature of Science
This domain tests how biological knowledge is built: hypothesis testing, experimental design, measurement quality, data interpretation, and peer review. Expect items that ask you to critique a described experiment, name variables, read a data table, or decide whether a stated conclusion follows from the evidence.
Experimental Design
- A hypothesis is a testable, falsifiable prediction about a specific relationship. A scientific theory is a broad, well-supported explanatory framework - not a guess.
- Independent variable: manipulated. Dependent variable: measured. Controlled variables: held constant. A control group receives no treatment or a placebo.
- Random assignment and adequate sample size reduce bias; replication improves reliability; single or double blinding blocks expectation effects.
Measurement and Data
- Accuracy is closeness to the true value; precision is repeatability. Systematic error shifts all readings one way; random error scatters them.
- Report mean ± standard deviation or standard error. Non-overlapping 95% confidence intervals suggest a real difference.
- Correlation is not causation - a confounding variable can drive both.
Worked Example
A student claims a fertilizer boosts plant growth. Design: 60 genetically similar seedlings in identical pots, soil, light, and watering schedule, randomly split into two groups of 30; Group A gets 10 mL fertilizer solution weekly, Group B gets 10 mL water. Height is measured after four weeks with the same ruler at the same time of day.
Reasoning: fertilizer is the independent variable and height the dependent variable; light, water, soil, and genotype are controlled; Group B is the control; random assignment spreads individual variation; n = 30 plus a fixed measurement protocol limit chance and measurement error.
Traps
- Writing that an experiment "proves" a hypothesis - data support or fail to support it.
- Mixing up a control group with a controlled variable.
- Using a huge but biased sample: size improves precision, not representativeness.
- Inferring cause from an observational correlation.