Experimental Programme and Scientific Inquiry Skills
This domain covers the scientific inquiry skills developed across the practical programme: designing investigations, collecting and processing data, and evaluating evidence. It is assessed in the IA and in Paper 3 Section A, where you interpret experimental data and suggest improvements.
Core rules and concepts
- Scientific method: formulate a hypothesis from theory; identify independent, dependent and controlled variables; design a method that is valid (tests the intended variable) and reliable (reproducible).
- Data collection: use appropriate apparatus; repeat measurements; record raw data with units and uncertainties; process data with correct significant figures.
- Analysis: identify trends, calculate gradients, use error bars, and compare experimental values with theoretical values.
- Evaluation: distinguish accuracy (closeness to the true value) from precision (reproducibility); distinguish systematic from random error; suggest specific improvements rather than generic ones.
- Communication: clear tables, graphs and conclusions linked to the research question.
Worked example
A student investigates the effect of temperature on the reaction between sodium thiosulfate and hydrochloric acid, timing how long the cross takes to disappear. The independent variable is temperature; the dependent variable is time (or 1/time as a rate); controlled variables include the volumes and concentrations of both solutions, the same cross and the same observer. A systematic error would be a thermometer reading 0.5 °C too high; a random error would be variation in human reaction time when starting and stopping the stopwatch. The reasoning is marked because it names each variable and classifies the error with its effect.
Traps that cost marks
Do not call a variable “controlled” when you mean “kept constant”-in IB, controlled variables are the ones held constant. Do not confuse validity with reliability. Do not propose “more repeats” as the only improvement; explain what would be measured differently. Do not ignore anomalous data-identify and justify excluding it. Do not state a conclusion that does not answer the research question.