Scientific Inquiry and Processes
What this domain tests
The Scientific Inquiry and Processes domain tests how chemistry knowledge is generated and validated: experimental design, measurement, uncertainty, data analysis, and safe laboratory practice. Items ask you to identify variables and controls, convert units, apply significant-figure rules, calculate percent error and density, and interpret graphs and tables.
Experimental design and variables
- The independent variable is manipulated; the dependent variable is measured; controlled variables are held constant so they do not confound the result.
- A control group gives a baseline for comparison; repeated trials reduce random error; blinding and random assignment reduce bias.
- A valid hypothesis is testable and falsifiable; an experiment supports or refutes it but never proves it.
Measurement and significant figures
- SI base units include the kilogram, meter, second, kelvin, mole, ampere, and candela; derived units include the newton, joule, pascal, and liter.
- Precision is reproducibility; accuracy is closeness to the accepted value. Random error scatters measurements and lowers precision; systematic error shifts every reading in one direction and lowers accuracy.
- Multiplication and division: round to the least number of significant figures. Addition and subtraction: round to the least number of decimal places. Exact conversion factors (1 L = 1000 mL) have unlimited significant figures.
- Dimensional analysis chains conversion factors so units cancel.
- On a graph, slope carries units of y divided by x; for a mass-versus-volume plot, slope equals density.
Worked example
Three density trials give 8.72, 8.75, and 8.74 g/mL; the accepted value is 8.96 g/mL. Mean = 8.737 g/mL, which rounds to 8.74 g/mL. Percent error = |8.74 − 8.96| / 8.96 × 100 = 2.5%. The trials agree within 0.03 g/mL, so they are precise, but all lie below the accepted value; that pattern suggests systematic error, such as an uncalibrated balance, rather than random scatter.
Traps that cost marks
- Swapping precision and accuracy, or blaming a systematic offset on random error.
- Reporting too many digits, or treating a measured conversion factor as exact.
- Reading a meniscus from above instead of at eye level, or measuring from the top of the curve.
- Averaging an outlier without justification, or describing correlation as causation.