Scientific Inquiry and Practical Skills
This domain tests whether you can design a fair test, name variables correctly, present and read data, and judge how far a conclusion is supported by the evidence given.
Designing an investigation
A hypothesis is a testable prediction, not a question. The independent variable is what you change, the dependent variable is what you measure, and controlled variables are everything else kept the same to make the test fair. A control group receives no treatment or a known standard, so the effect of the treatment can be isolated. Repeat and use several samples to check reliability; record raw data in a table with units in the headings.
Presenting and reading data
A bar chart suits categories (species, treatment groups); a line graph suits continuous data such as temperature or time. Label both axes with units and give the graph a title. Describe trends in words with figures from the graph, then explain the trend using biology. Calculate a mean by adding the values and dividing by the number of values; exclude clear anomalies and say that you did.
Evaluating sources
Distinguish primary data (collected yourself) from secondary (textbooks, articles, websites) and check whether the source is reliable and current. Correlation between two variables does not prove that one causes the other.
Worked example
A student tests whether fertiliser affects shoot height: ten seeds in compost with fertiliser, ten in plain compost, all on one windowsill for three weeks. Reasoning: independent variable = fertiliser; dependent variable = shoot height in cm; controlled variables = seed type, soil volume, water, light, temperature, time. The plain-compost group is the control. Heights of 8.2, 8.6, 9.0, 9.1, 8.8, 8.4, 9.3, 8.9, 21.0 and 8.7 cm give a mean of 8.8 cm once the 21.0 cm outlier is excluded and the exclusion is reported.
Traps
Swapping the independent and dependent variables; calling a controlled variable a control group; using a line graph for non-continuous categories; averaging an obvious outlier silently; writing a conclusion that goes beyond the data; asserting cause from correlation.