Scientific Inquiry, Data Analysis, and Instruction
Scientific Inquiry, Data Analysis, and Instruction
This domain tests experimental design, measurement quality, data interpretation, and evidence-based classroom instruction.
Core concepts
- Inquiry proceeds through observation, question, hypothesis, prediction, experiment, analysis, conclusion, and peer review. A hypothesis is testable; a theory is a well-supported explanation; a law describes a relationship.
- Variables: the independent variable is manipulated, the dependent variable is measured, constants are held fixed, and a control group receives no treatment or a standard treatment.
- Validity means the experiment tests what it claims; reliability means repeated results are consistent. Random assignment and adequate sample size reduce bias.
- Accuracy is closeness to the true value; precision is reproducibility. Random error scatters results; systematic error shifts them.
- Data analysis includes tables, graphs, means, medians, ranges, outliers, trend lines, slope, and uncertainty. Correlation does not establish causation.
- Instruction should use phenomena, inquiry, the 5E model, formative assessment, and explicit attention to misconceptions.
Worked example
Students test whether fertilizer affects bean plant height. Group A receives 10 mL of fertilizer solution; Group B receives 10 mL of water. Both groups get the same soil, light, temperature, and pot size. The independent variable is fertilizer amount, the dependent variable is plant height, the control is Group B, and the constants are light, soil, temperature, and water volume. Reasoning: change one variable, measure the outcome, and hold everything else constant.
Traps
Do not confuse precision with accuracy or correlation with causation. A hypothesis is not merely a guess, and an experiment rarely "proves" a theory. Confounding variables can create false cause-effect conclusions. Watch significant figures and metric conversions. Do not label the dependent variable as the one the teacher changes.