Scientific Inquiry and Methodology: Designing and Interpreting Investigations
What this domain covers
Scientific Inquiry and Methodology tests whether you can design fair tests, interpret data, and reason about evidence. Expect questions on variables (independent, dependent, controlled), hypothesis formation, measurement and units, graphing and data tables, sources of error, and the difference between observation and inference. You may also see items on experimental vs. observational studies and on evaluating whether a conclusion follows from data.
Concepts to master
Know that a good experiment changes one independent variable while holding all others constant, and that the dependent variable is what you measure. Understand repetition and sample size as tools to reduce random error, and recognize that systematic error (e.g., a miscalibrated scale) biases results in one direction. Be able to read a graph's axes, identify trends, and distinguish correlation from causation. Know that a control group provides a baseline for comparison.
Common traps
Questions often present a flawed design - two variables changed at once - and ask what is wrong. Another trap is a conclusion that overreaches the data (e.g., claiming proof from a single trial). Watch for confusion between a hypothesis (a testable prediction) and a theory (a well-supported explanation). Also, be careful not to treat a control group as unnecessary when a comparison is needed.
Worked rule of thumb
Use the "one change, one measure, rest same" checklist. Example: testing whether fertilizer affects plant growth - fertilizer amount is independent, plant height is dependent, and light, water, and soil must be controlled. If two pots differ in both fertilizer and sunlight, the result is confounded and cannot support a causal claim.