Geography Coursework Evaluation Example

1. Evaluation

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 techniqueQuestionnaires 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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