Foundations of Scientific Inquiry
Domain 1 examines the methods behind Earth and space science claims: how hypotheses are framed, how measurements are made and reported, how uncertainty is handled, and how maps, graphs, and models turn data into evidence. Expect items that ask you to choose the best conclusion from a described data set or to identify a flaw in an experimental design.
Measurement and uncertainty
- SI base units include meter, kilogram, second, ampere, kelvin, mole, and candela. Derived units include N, Pa, J, and W.
- Precision is repeatability; accuracy is closeness to the accepted value. A tight cluster far from the target is precise but inaccurate.
- Significant figures follow the measuring tool. Multiplication and division keep the least number of sig figs; addition and subtraction keep the least decimal place.
- Percent error = |measured − accepted| ÷ accepted × 100%, reported with sensible sig figs.
- Scientific notation removes ambiguity: 0.00450 m = 4.50 × 10⁻³ m, three sig figs.
Maps, graphs, and models
- A map scale is a ratio; area scales by the square of the linear factor.
- Contour lines connect equal elevation; the contour interval is constant, and index contours are labeled. Closely spaced contours mean steep slope; hachures mark depressions.
- A cross section is built by projecting contour elevations along a line. Models simplify, so state assumptions and limits.
Worked example
A specimen has mass 24.6 g and volume 8.2 mL. Density = 24.6 g ÷ 8.2 mL = 3.0 g/mL, limited to two sig figs by 8.2 mL. The accepted density is 2.7 g/mL. Percent error = |3.0 − 2.7| ÷ 2.7 × 100% ≈ 11%, reported as about 10% at two sig figs. The specimen is not a close match. If repeated runs gave 2.98, 3.01, and 2.99 g/mL, the method would be precise but still biased high.
Traps that cost marks
- Squaring the scale factor for area, or forgetting to convert km to cm before using a ratio.
- Calling a hypothesis a theory, or treating a law as a proven final truth.
- Forcing a line of best fit through every point rather than showing a trend.
- Confusing correlation with causation in a field study.
- Ignoring that a model's prediction is only as good as its assumptions.