Nature of Science
Nature of Science
The Nature of Science domain tests how chemistry claims are built and checked: experimental design, measurement quality, data interpretation, and the limits of models. Questions often ask you to identify variables, apply significant-figure rules, or judge whether a conclusion follows from data.
Experimental design and evidence
- A hypothesis is a testable prediction; a theory is a broad, well-supported explanation; a law describes a consistent relationship.
- The independent variable is manipulated, the dependent variable is measured, and controlled variables are held constant. Replication and peer review reduce bias.
- Correlation does not establish causation; a model is useful but approximate.
Measurement and error
- Accuracy is closeness to the accepted value; precision is reproducibility. Random error scatters results; systematic error shifts them consistently and averaging does not remove it.
- Percent error = |experimental - accepted| / accepted x 100.
- For multiplication and division, keep the least number of significant figures; for addition and subtraction, keep the least number of decimal places.
Worked example
A metal sample has accepted density 2.70 g/mL. Three trials give 2.45, 2.47, and 2.44 g/mL. Mean = 2.453 g/mL. Percent error = |2.453 - 2.70| / 2.70 x 100 = 9.1 percent. The trials are precise but inaccurate, suggesting systematic error such as a miscalibrated balance. Report the mean as 2.45 g/mL, because the data have three significant figures.
Traps that cost marks
Leaving the absolute value out of percent error; using the experimental value as the denominator; treating trailing zeros as always significant; rounding intermediate steps; confusing precision with accuracy; and assuming a linear fit justifies extrapolation far beyond the data.