Nature of Science
The Nature of Science domain tests how scientific questions are framed, how investigations are designed, and how data become evidence. Expect items on variables, controls, measurement, graphs, error, models, and the distinction between theories and laws.
Core rules and concepts
Scientific inquiry starts with a testable question. A hypothesis is a falsifiable prediction based on prior knowledge, not a random guess. In a controlled experiment, the independent variable is manipulated, the dependent variable is measured, and controlled variables are held constant. A control group provides a baseline. Replication and repeated trials reduce the influence of random error; random assignment and blinding reduce bias.
Measurement uses SI base units: meter, kilogram, second, ampere, kelvin, mole, and candela. Derived units include newton, joule, watt, and pascal. Accuracy is closeness to the true value; precision is repeatability. Significant figures should reflect the instrument’s precision, and every number needs a unit. Line graphs suit continuous data, bar graphs categorical data, and scatterplots show relationships between two variables.
Scientific models simplify systems; theories explain bodies of evidence, while laws describe consistent relationships. Both are supported by evidence and remain open to revision. Peer review, reproducible methods, and transparent data are central to scientific communication.
Worked example
A student predicts that more fertilizer will increase bean plant height. She grows one plant at 0 g, one at 5 g, and one at 10 g fertilizer, keeping light, water, soil, and pot size constant. Independent variable: fertilizer amount. Dependent variable: plant height. Controlled variables: light, water, soil, pot size, plant species. The 0 g treatment is the control. Her conclusion is limited because each treatment has only one plant; individual variation and measurement error cannot be separated from the fertilizer effect. She should use several plants per treatment, measure with a consistent ruler, and report means and ranges.
Traps that cost marks
Common traps include calling a hypothesis a theory, confusing accuracy with precision, ignoring confounding variables, and claiming a study “proves” a hypothesis. Correlation does not establish causation. Also check graph axes, units, and whether a conclusion exceeds the data.