A raw data table is evidence. It should let someone see what was measured before processing decisions were made.

Raw data should be recorded directly, not only as processed values.

Each column needs a quantity name and unit.

Instrument resolution or measurement uncertainty should be recorded.

Repeat trials should be kept visible rather than only reporting an average.

Record relevant conditions and settings, such as temperature, calibration, sampling rate, or alignment.

Anomalies should be flagged and investigated, not silently removed.

Repair data-collection records.

Spot Errors

Describe how to collect raw data for a physics investigation.

Recording only processed results.

Describe how to collect raw data for a physics investigation.

Choose

Processing is the bridge from measurement to claim. A reader should see how raw values became a conclusion.

Processed data should be traceable from raw data through formulas or spreadsheet methods.

Show at least one sample calculation for derived quantities.

Calculate averages and uncertainty estimates where repeats are used.

Graphs should be chosen to test the expected relationship.

Gradient, intercept, and trend should be interpreted in terms of the physics model.

Interpretation should answer the research question and acknowledge uncertainty.

Repair processing and interpretation statements.

Spot Errors

Explain how raw data should be processed and interpreted.

Showing processed values without a link to raw data.

Explain how raw data should be processed and interpreted.

Choose

Data quality language should be precise. Saying “good data” is not enough; name which quality dimension is strong or weak and cite evidence.

Precision describes the spread or repeatability of measurements.

Accuracy describes closeness to the true or accepted value.

Reliability improves when repeated measurements give consistent results.

Validity asks whether the method measures the intended relationship fairly.

Random errors affect scatter; systematic errors bias results in one direction.

Outliers should be investigated using method evidence, not removed automatically.

Repair data-quality evaluations.

Spot Errors

Evaluate the quality of data in an investigation.

Using accuracy, precision, reliability, and validity interchangeably.

Evaluate the quality of data in an investigation.

Choose

This summary keeps the evidence chain intact: raw data, processed data, interpretation, quality.

Collect raw measurements with units, uncertainties, repeats, and relevant conditions.

Keep raw repeats visible and record anomalies honestly.

Process data with traceable calculations, averages, uncertainties, and suitable graphs.

Interpret gradients, intercepts, and patterns in terms of the physics model.

Evaluate quality using accuracy, precision, reliability, validity, random error, and systematic error.

Match each collecting/processing cue to its function.

Match

Reasons

0/6

Summarize how data should be collected, processed, and evaluated.

Skipping from raw data to conclusion.

Summarize how data should be collected, processed, and evaluated.

Choose

IB Physics HL S2.2 Collecting and processing data Notes

Practice Collecting data

Practice Processing and interpreting data

Practice Data quality

Retrieve the Collecting and processing data Model