Foundations of Scientific Inquiry and the Nature of Science
Foundations of Scientific Inquiry and the Nature of Science
The NYSTCE Biology CST uses this domain to test whether you can design, interpret, and critique biological investigations. Questions often present an experiment, graph, or claim and ask you to identify variables, evaluate evidence, or choose valid conclusions. You must distinguish scientific explanations from pseudoscience and recognize how peer review, models, and technology shape biological knowledge.
Core rules and concepts
- A hypothesis is a testable, falsifiable explanation; a prediction is a statement about expected results if the hypothesis is true.
- The independent variable is manipulated; the dependent variable is measured; controlled variables are held constant; a control group provides a baseline without the treatment.
- Reliability means repeated measurements agree; validity means the design measures what it claims. Accuracy is closeness to the true value; precision is closeness among repeated values.
- Choose graphs by data type: bar for categorical data, line for continuous change, scatter for correlation. Error bars show variation or uncertainty.
- Scientific theories are broad, well-supported explanations, not guesses; laws describe regularities. Correlation does not establish causation.
Worked example
A student hypothesizes that higher light intensity increases photosynthesis in Elodea. She places equal masses of plant in tubes at 10 cm, 20 cm, and 30 cm from a lamp, counts oxygen bubbles per minute, and keeps temperature and sodium bicarbonate concentration constant. A tube with no Elodea is included. The independent variable is distance from the lamp, a proxy for light intensity; the dependent variable is bubbles per minute. Temperature and bicarbonate are controlled variables. The no-plant tube is the control: bubbles there would reveal an artifact. If bubbles decrease as distance increases, the data support the hypothesis, but replication and a statistical test are needed before claiming significance.
Traps that cost marks
- Confusing the control group with controlled variables.
- Treating a hypothesis as a prediction, or a theory as a guess.
- Claiming causation from a correlational study.
- Ignoring sample size, error bars, or confounding variables.