Nature of Science: investigation, data and evaluation
Nature of Science (NOS) is the unifying strand of Junior Cycle Science: it is examined through every other strand, so questions about variables, measurement, graphs and conclusions can appear in a biology, chemistry or physics context. Expect to design a fair test, identify variables, handle data with units, and judge whether a conclusion is supported.
Designing a fair test
A hypothesis is a testable prediction, not a guess ('Increasing light intensity increases the rate of photosynthesis'). The independent variable is what you change, the dependent variable is what you measure, and controlled variables are kept constant so the test is valid. Repeat readings and take a mean to improve reliability. A control group is untreated, showing the change is due to your variable.
Data, graphs and reliability
Quantitative data are numbers (length, °C); qualitative data describe (colour, smell). Continuous data suit line graphs; discrete or categorical data suit bar charts. Label axes with quantity and unit, use an even scale, plot points and add a line of best fit. Anomalies are ignored only with a stated reason. Accuracy means closeness to the true value; precision means repeats are close to each other.
Worked example
A student tests whether light intensity affects duckweed growth using lamp distances of 10, 20, 30 and 40 cm. Independent variable: distance; dependent: number of new fronds after 7 days; controlled: same volume of pond water, temperature, starting frond count and lamp. Three repeats per distance, mean calculated, the 30 cm result identified as an anomaly with a reason. Conclusion: within 10–40 cm, growth decreases as distance increases - it must not claim anything beyond that range.
Mark-losing traps
- Writing 'proves' instead of 'supports'.
- Confusing accuracy with precision.
- Missing units, or failing to control a variable.
- Drawing conclusions outside the range tested.