Go back through each one of your methods. For each, consider
- How accurate were your results?
- What were the sources of error?
- How has each source of error affected your results?
Definitions
True value: the value that would be obtained in an ideal measurement
Accuracy: how close a measurement is to the true value
Error: the difference between the result that you found and the true value
What are the sources of error?
- Measurement error: mistakes made when collecting the data (such as someone misreading a clinometer)
- Operator error: differences in the results collected by different people (such as different people giving different environmental quality scores)
- Sampling error: where a sample is biased. Some elements of the population are less likely to be included than others.
Validity: the suitability of the method to answer the question that it was intended to answer
How do sources of error affect results?
Suggest how errors could have changed the results. Here is an example.
| Fieldwork technique | Questionnaires were given out to every 5th person seen in the High Street on Sunday morning |
| Bad evaluation |
It was raining heavily when I gave out questionnaires |
| Good evaluation |
It was raining heavily when I gave out questionnaires, so there were few people in the High Street except for rough sleepers. My sample was biased because shoppers were under-represented. Therefore the results of the questionnaire survey may not be accurate. |
Random error: these cause results to be spread about the true value. For example, imagine a student takes 20 temperature readings and mis-reads the thermometer for 2 of the readings. The effect of random errors can be reduced by taking more measurements.
Systematic error: these cause results to differ from the true value by a consistent amount each time the measurement is made. For example, imagine a student uses weighing scales which have not been zeroed, so all the results are 10g too high. The effect of systematic errors cannot be reduced by taking more measurements.
Anomalies
These are values in a set of results which are judged not to be part of the variation caused by random uncertainty.
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