Foundations of Scientific Inquiry and the Nature of Science
This domain tests whether you can frame testable questions, design controlled investigations, interpret evidence, and distinguish scientific claims from pseudoscientific ones. Expect items on variables, measurement, models, peer review, and the limits of inference.
Core rules and concepts
- A hypothesis must be falsifiable and lead to predictions; a theory is a broad, well-supported explanation, not a guess.
- Identify the independent variable (manipulated), dependent variable (measured), constants, and control group.
- Reliability means repeated measurements agree; validity means the investigation measures what it claims to measure.
- Correlation does not establish causation; confounding variables and sample bias can create misleading patterns.
- Scientific models simplify systems and are evaluated by explanatory and predictive power, not by whether they are “perfect.”
Worked example
A student predicts that sandstone weathers faster than granite in acid rain. She places equal-sized cubes of each rock in identical volumes of pH 4 solution at 20°C and measures mass loss after seven days. The rock type is the independent variable; mass loss is the dependent variable; solution pH, temperature, cube size, and duration are constants. A useful control is the same rocks in pH 7 water, which shows whether the acid treatment itself changes weathering. Replication reduces random error. If sandstone loses more mass, the data support-but do not prove-the prediction.
Traps that cost marks
- Saying an experiment “proves” a hypothesis; evidence supports or fails to support it.
- Confusing a control group with a controlled variable.
- Treating a single anecdote or unpeer-reviewed claim as scientific evidence.
- Ignoring units, uncertainty, or the difference between precision and accuracy.