Scientific Inquiry, Methodology, and the Nature of Science
Experimental Reasoning
Items here ask you to design, critique, and interpret investigations, and to tell scientific claims apart from pseudoscientific ones. The content is process and reasoning, not vocabulary recall.
Core rules and concepts
- A hypothesis is a testable, falsifiable statement about a relationship; a theory is a broad, repeatedly supported explanatory framework (cell theory, evolution by natural selection); a law states a consistent regularity, often mathematically. Science supports or fails to support claims - it does not "prove" them.
- The independent variable is manipulated by the researcher; the dependent variable is measured; controlled variables are held constant; the control group receives no treatment, a placebo, or a baseline condition.
- Random assignment can reduce systematic allocation bias and supports causal inference in a well-designed experiment. Observational association alone does not establish causation; additional design, assumptions and evidence are needed for causal interpretation.
- Accuracy is closeness to the true value; precision is reproducibility. Random error degrades precision; systematic error (miscalibrated instrument, biased thermometer) degrades accuracy.
- Replication and adequate sample size reduce the chance that a result reflects random variation; descriptive statistics (mean, standard deviation) summarize data, while inferential statistics (t-test, chi-square) test hypotheses.
- Deduction moves from a general principle to a specific prediction; induction builds general patterns from specific observations. In a school engineering-design comparison, criteria describe desired performance and constraints restrict choices, such as a supplied cost or mass limit. Compare alternatives using measured evidence, identify tradeoffs and revise a model. A design satisfying one criterion can still violate a constraint. This is introductory design reasoning rather than a complete teaching-performance assessment.
Worked example
A student counts oxygen bubbles released per minute by Elodea at five distances from a lamp and concludes that light intensity drives photosynthesis. Distance is the independent variable; bubble rate is the dependent variable. Controls that must be held constant include water temperature, dissolved CO₂ (bicarbonate concentration), plant mass, and counting interval. The design has a confound: an incandescent lamp also heats the water, so temperature, not light, could explain the trend. Fixing it requires a heat filter or water bath, plus independently replicated observations and a justified sample-size choice, reporting variation alongside means. Three replicates can illustrate a calculation but are not a universal guarantee of sufficient power. Even then the defensible conclusion is that light intensity is associated with increased photosynthetic rate in this system - a claim about a sample, not proof of a universal law.
Traps that cost marks
Calling a hypothesis a theory, or saying a result "proves" a hypothesis. Confusing the control group with controlled variables. Accepting causation from correlation. Ignoring a confound such as temperature, pH, or time of day. Treating a single trial as evidence. Forgetting that the sample must be representative before results are generalized.