Working scientifically: fair tests and evidence
Working scientifically
This domain tests whether you can turn an idea into a fair test and then interpret the evidence without overclaiming. Expect questions on variables, controls, sampling, tables and graphs, averages, anomalies, accuracy and precision, and evaluation of method and conclusion.
Core rules
- Independent variable: what you change; dependent variable: what you measure; controlled variables: kept constant so they do not confound the result.
- A control group is not the same as a controlled variable. A control is a comparison treatment with the independent variable absent or set to a standard.
- Repeat measurements and calculate a mean to reduce the effect of random error. Identify anomalies before averaging.
- Accuracy is closeness to true value; precision is closeness of repeated values. A precise method can still be inaccurate if there is a systematic error.
- Validity depends on the method measuring what it claims and controlling confounders.
- Choose a sample size and sampling method that avoid bias.
Worked example
A student investigates light intensity and pondweed photosynthesis by counting oxygen bubbles per minute at five distances from a lamp. Distance is the independent variable; bubble rate is dependent. Temperature, CO2 concentration and pondweed length must be controlled. The student repeats each distance three times and uses the mean, ignoring one obvious high count caused by a bubble leak. She plots rate against distance and describes the trend rather than claiming a precise mathematical law. Reasoning: the bubble count is a proxy for oxygen production; heat from the lamp could change temperature, so a water bath or heat filter is needed.
Traps
- Calling the dependent variable the thing you change.
- Treating one measurement as reliable.
- Saying a result is proved from a small sample.
- Confusing precision with accuracy.
- Forgetting units on table headings and axes.