Foundations of Scientific Inquiry
Foundations of Scientific Inquiry tests whether you can reason like a working scientist: turning questions into falsifiable hypotheses, designing fair tests with controls, reporting measurement and uncertainty honestly, and judging claims about science and technology. Items ask you to interpret tables and graphs, convert units, and separate scientific explanations from non-scientific ones.
Designing a fair test
- The independent variable is deliberately changed; the dependent variable is the measured response.
- Controlled variables are held constant so they cannot confound the result; a control group receives no treatment or the standard treatment.
- Larger samples and repeated trials reduce the influence of random error.
- A hypothesis must be falsifiable - some possible outcome must be able to count against it.
Explanations and measurement
- A theory is a well-tested explanatory framework (cell theory, plate tectonics), not a guess; a law states a consistent relationship, often mathematically, without explaining why.
- SI base units: meter, kilogram, second, ampere, kelvin, mole, candela.
- Precision is reproducibility; accuracy is closeness to the accepted value. Percent error = |measured − accepted| ÷ accepted × 100%.
- Significant figures reflect instrument precision; carry units through conversions and cancel them.
Worked example
A student measures the density of an aluminum sample three times: 2.68, 2.69, and 2.70 g/cm³; the accepted value is 2.70 g/cm³. The mean is 2.69 g/cm³, so percent error = |2.69 − 2.70| ÷ 2.70 × 100% ≈ 0.37%. The three trials span only 0.02 g/cm³, so they are precise, and the small error shows they are accurate as well. The result should be reported as 2.69 g/cm³ - two decimal places matching the instrument, not 2.6900.
Traps that cost marks
- Claiming a hypothesis is "proven"; data support or fail to support it.
- Describing a theory as an unproven guess, or ranking theories below laws.
- Reporting more significant figures than the instrument justifies.
- Inferring causation when a variable was left uncontrolled.