Nature of Science and Engineering
What this domain tests
The Nature of Science and Engineering domain asks you to reason about how scientific knowledge is built and how engineers solve problems under constraints. Expect questions on experimental design, measurement, data interpretation, and the distinction between evidence and explanation.
Inquiry and experimental design
A testable hypothesis must be falsifiable. In a controlled experiment, change one independent variable, measure the dependent variable, and hold confounding variables constant. Use a control group when a comparison is needed. Replication, random sampling, and peer review reduce bias. Correlation does not establish causation. A scientific theory is a well-supported explanation; a law describes a repeated relationship. Both can be revised by new evidence.
Measurement and uncertainty
Use SI units and dimensional analysis. Accuracy is closeness to the accepted value; precision is repeatability. Random error scatters data; systematic error shifts data consistently. For multiplication and division, round to the fewest significant figures; for addition and subtraction, round to the least precise decimal place. Percent error = |measured - accepted| / accepted x 100%.
Engineering design
Define the problem, identify criteria and constraints, brainstorm, prototype, test, and iterate. Trade-offs are expected: a design that maximizes strength may add mass or cost. Failure analysis informs redesign.
Worked example
A rectangular block measures 2.50 cm x 1.20 cm x 0.80 cm and has mass 14.4 g. Find density and percent error if the accepted density is 7.0 g/cm3. Volume = 2.50 x 1.20 x 0.80 = 2.40 cm3. The measurement 0.80 cm has two significant figures, so report 2.4 cm3. Density = 14.4 g / 2.4 cm3 = 6.0 g/cm3. Percent error = |6.0 - 7.0| / 7.0 x 100% = 14%.
Traps
Do not say a hypothesis is proven. Do not confuse precision with accuracy. Do not ignore significant figures when a factor limits certainty. Do not treat a model as a perfect copy of reality.