Environmental Monitoring

Environmental Monitoring

You need to be aware about the kinds and sources of errors and how to avoid them. There are two general kinds of error you need to be aware of when designing your program:

  • errors in data collection;
  • errors in decision making.

Errors in data collection

  • Human Error - repeated readings and data checking should identify these errors, e.g. 575 transposed to 757.
  • Instrumentation Limitation (Rounding errors) - be aware of the limitations (sensitivity) of your technique.
  • Uncontrolled Factors - even the best designs will leave some variables uncontrolled, e.g. experiments on different days or different times of the same day.
  • Unrepresentative Samples- this should not occur if the sampling is controlled; be certain of the limits of the population from which you are sampling.
  • Statistical Fluctuations (random or experimental errors) - most statistical tests assume that they exist.
  • Systematic Errors (accuracy/precision) - Is the equipment calibrated? Does it measure what you think it is measuring? This type of error will introduce unacceptable bias.

More information is available here.

Errors in decision making
The "error" referred to in this context is how often you wrongly reject a correct hypothesis (Type I error) or do not reject a false hypothesis (Type II error). These issues are considered under the topic of "power".

In the end experimental design and properly instituted QA/QC are essential to managing errors and ensuring useful data. A summary.

Hurlbert (1984) summarises the potential sources of "confusion" in an experiment and means to eliminate or minimise their effect.

How do I choose the best methods?


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