Analysis, communication and evaluation
- Syllabus
- 2024
- Topic
- —
- Level
- —
Learning objectives
—Analyse data and draw conclusionsUse scientific knowledge and understanding to analyse and interpret data and draw conclusions consistent with experimental evidence.—Communicate experimental findingsCommunicate experimental findings using appropriate technical language, relevant calculations and graphs.—Assess experimental reliabilityAssess the reliability of an experimental activity.—Evaluate accuracy and validityEvaluate data and methods, taking into account factors that affect accuracy and validity.
Analyse data and draw an evidence-based conclusion
Analyse experimental data by identifying the pattern, testing it against every result and using relevant scientific knowledge to explain what the evidence supports.
| Move | What to do |
|---|---|
| describe | state how the dependent result changes as the independent variable changes; include direction and any plateau, maximum or minimum |
| check | identify results that do not fit the overall pattern and verify that they are genuinely anomalous |
| interpret | connect the pattern to the relevant chemical idea without claiming more than the data show |
| conclude | answer the investigation question and cite the trend or observation that supports the answer |
A graph may show that increasing acid volume decreases conductivity over a stated range, or that longer carbon chains take longer to produce a fixed gas volume. An observation such as no zinc remaining supports the conclusion that zinc was limiting and acid was in excess.
Do not describe one point as a trend, ignore an inconvenient result, or turn association into unsupported causation. A conclusion must remain consistent with the tested range and experimental evidence.
Communicate findings with calculations and graphs
Communicate findings so another reader can reconstruct what was measured, how values were processed and what the graph or calculation shows.
| Form | Required features |
|---|---|
| results table | descriptive headings with units, consistent precision, one row per condition or trial |
| calculation | relationship or formula, substitution, working, unit and appropriately precise result |
| graph | independent variable on x, dependent variable on y, labelled units, sensible linear scales using the grid, small accurate points |
| best fit | a straight line for an approximately linear relationship or a smooth curve for a changing gradient; balance the overall pattern rather than join point-to-point |
Exclude a justified anomaly when fitting the pattern. Read an intersection, maximum or plateau from the best-fit representation at the precision supported by the grid, and report both value and unit.
A curve of best fit is not a chain of straight segments. Do not force every point onto the line, use uneven unmarked scales, omit units, or hide a calculation behind a bare answer.
Assess whether experimental results are reliable
Reliable results are consistent when a measurement or investigation is repeated under the same conditions.
| Evidence | Reliability judgment |
|---|---|
| repeated values are close together | good repeatability; the result is more reliable |
| repeated values have a wide spread | poor repeatability; the result is less reliable |
| one value is far from the others | investigate it as a possible anomaly before calculating a representative value |
| only one measurement exists | reliability cannot be assessed directly from repeats |
Repeat each condition, identify anomalies using the pattern and repeat evidence, then calculate a mean from justified consistent results. More repeats strengthen the estimate only when the method and conditions remain the same.
A repeated measurement can be tightly grouped yet systematically wrong. Reliability concerns consistency; it does not by itself prove accuracy or validity.
Evaluate accuracy and validity, then improve the method
Evaluate an experiment by identifying a specific weakness, explaining how it changes the measured result, and proposing a change that directly reduces that effect.
| Idea | Question | Example weakness | Targeted improvement |
|---|---|---|---|
| accuracy | how close is the result to the accepted or true value? | gas escapes before the syringe is connected, so measured volume is too low | connect the apparatus quickly and check for leaks |
| validity | does the method measure the intended effect in a fair comparison? | initial temperatures differ, so temperature change is not caused only by the tested variable | equalise or record both initial temperatures and compare changes |
| systematic effect | does the same bias act in one direction? | product retains water, making mass and percentage yield too high | dry to constant mass before weighing |
| random effect | do readings vary unpredictably? | judging a fluctuating maximum temperature | stir for an even temperature and repeat the measurement |
Use the chain weakness → effect on data → improvement. Cotton wool can stop acid spray while allowing gas to escape, so mass loss represents the gas more accurately; a condenser reduces loss of water vapour and raises collected yield.
“Human error”, “be more careful” and “repeat” are not complete accuracy improvements unless the source and direction of error are explained. Repeating improves reliability but does not remove a systematic bias or an invalid design.