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TWS 6: Evaluation — Assessing Quality of Data, Errors & Improvements

5 min read 5 parts Lesson 6 of 8

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TWS 6: Evaluation — Assessing Quality of Data, Errors & Improvements — Alhussan International Academy — Science (Stages 7–9) notes: Judge the quality of data using anomalies, spread, range and number of values, distinguish random from systematic error, evaluate methods and sugges…

  • Evaluating
  • Anomalous results
  • Uncertainty
  • Error check
  • Instruments and methods
  • Other equipment

Part 1 of 5The three stages of an evaluation

  • Evaluating working out what is good and what is not so good about an investigation.
  • Stagesevaluate the quality of the data, evaluate the methods used and suggest improvements to the method.
  • Aiman improvement should make you more confident in your conclusion if the investigation were done again.

Part 2 of 5Evaluating the data: anomalies, spread and range

1Anomalous results1 point
— check the data tables and graphs for them, which is why anomalous results are never erased.
  • They can limit your conclusions and reduce confidence in them.
2Spread1 point
= the difference between the smallest and biggest values of repeated measurements.
  • A small spread (results close together) gives more confidence in a conclusion than a large spread.
3Range1 point
= the difference between the smallest and largest values of the variable investigated.
  • A large range gives more confidence in the conclusion.
4Number of values
— collect enough data points to be confident the trend or pattern is correct, since two would not be enough.

Part 3 of 5Uncertainty and errors

  • Uncertainty — there is uncertainty in any measurement, which is one reason for a spread in data.
  • Error check — think about possible errors, anomalous results and the spread when deciding how confident to be.
Error typeEffectExample
Random errorAffects the spread or causes anomalous results; unpredictableRoom temperature changes suddenly because someone opens a door
Systematic errorMakes measurements less accurate, shifting results the same wayA newtonmeter reads 1 N with nothing attached
  • Repeats — repeating readings and taking a mean reduces random error but not systematic error.

Part 4 of 5Evaluating the methods

  • Instruments and methods — look back at the choices made when planning.
  • Extent — describe how far the equipment enabled accurate and precise data, and how far the methods did too.

Part 5 of 5Suggesting improvements

1Purpose1 point
— improvements are not about making the experiment easier or quicker but about improving the quality of the data.
  • They leave fewer limitations to the conclusion and more confidence in it.
2More5 points
Better data with the same equipment:
  • eliminating systematic errors;
  • reducing the effect of random errors;
  • including a bigger range;
  • doing more repeat readings;
  • repeating any measurements that gave anomalous results.
3Other equipment4 points
— some equipment gives more precise and accurate data than other equipment, and research may be needed.
  • Light gates measure speed more accurately than a hand-clicked stopwatch.
  • A measuring cylinder measures volume more accurately than a beaker.
  • A balance reading more decimal places gives a more precise mass.
  • Video of a falling object can be replayed to see where it was.
4Reading
— looking directly at the scale improves volume readings.

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