Question

Define Type I error and Type II error. Explain why both ‘‘false positive’’ and ‘‘false negative’’ should be avoided in the analysis and monitoring of environmental contaminants?

Answer 1

In a statistical hypothesis testing

a type 1 error is the rejection of a true null hypothesis also known as false positive it is falsely inferring the existence or reality of something that is in fact not real or does not in fact exist that means conforming to common belief with false information.. Examples of type 1 errors include a test that shows a patient to have a disease when in fact patient does not have the disease , wrong fire alarm etc.

a type 2 error is the failure to reject a false null hypothesis also known as false negative it is falsely infer the absence or non-existence of something that is real or does exist that means conclusion going against the common belief with false information. examples of type 2 errors would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease, fire alarm doesn't ring even when fire break out etc

In environmental contamination,

false positive error detect species when no target species eDNA is present in the sample, and it also detect target species eDNA when species is absent from the ecosystem. which ruin assay specificity and knowledge of the ecology of eDNA in the environment.

false negative error  fail to detect species when target species eDNA is present in the sample, and it also fail to detect species when present in the ecosystem because viable target species eDNA absent in sample which ruin assay specificity and sample collection , handling and processing methods

so, both false positive and false negative errors should be avoided in the analysis and monitoring of environmental contaminants