Paper 1B study guide
Chemistry experimental skills checklist
Use this to ask the right questions about a supplied investigation. It is about reasoning from experimental evidence, not memorizing a recipe.
Techniques named in the current guide
Standard solution & dilution
Know what quantity is fixed by the volumetric glassware, how concentration follows from n=CV, and what errors affect the prepared concentration.
Titration
Relate endpoint data to stoichiometry; distinguish reading precision from chemical validity of the endpoint.
Calorimetry
Use energy balance, interpret sign and identify heat transfer or heat-capacity assumptions.
Chromatography & separation
Interpret separation evidence and choose conclusions supported by observed components rather than by appearance alone.
Colorimetry / spectrophotometry
Use standards, calibration curves, R² and interpolation; recognize why extrapolation and systematic bias matter.
Electrochemical cells
Interpret cell data and, at HL, link standard potentials to thermodynamic direction.
Recrystallization / melting point
Use data to discuss purity and recovery without assuming one measurement proves identity.
Digital data & sensors
Read resolution, sampling interval, calibration and database provenance before treating data as exact.
Data-quality language that earns its place
| Concept | Useful interpretation |
|---|---|
| Random effects | Create scatter; repetitions can reveal and sometimes reduce their influence on a mean. |
| Systematic effects | Shift results in a common direction; averaging does not remove the bias. |
| Precision | How closely repeated values agree or how finely a measurement is resolved. |
| Accuracy | Closeness to an accepted/true value, where such a reference is meaningful. |
| Reliability | Consistency under repetition or replication. |
| Validity | Whether the design and measurements actually test the intended chemical question. |
Graph checklist
- Are axes correctly labelled with quantity and unit?
- Is a line or curve of best fit more defensible than joining points?
- Does an intercept have chemical meaning, or suggest systematic offset/model mismatch?
- Are uncertainty bars compatible with the claimed relationship?
- Is the requested value interpolated inside the data range or extrapolated beyond it?
- Does R² support the chosen trend without being overinterpreted?
Evaluation checklist
A useful improvement is specific: state the limitation, explain the effect it can have on the measured/processed result, and identify how the proposed change addresses that effect. “Human error” and “use better equipment” are usually too vague to explain the chemistry.