In science and environmental management conclusions and decisions may be based on the outcome of statistical tests of null hypotheses. These tests are prone to two types of error. A Type I error is the rejection of a true null hypothesis, while a Type II error is the failure to reject a false null hypothesis, put another way, to detect a true effect. These errors have different consequences; which set of consequences is of most concern varies with the objectives of the test.
A Type I Error is the Rejection of a True Null Hypothesis
Scientists are cautious to avoid making Type I errors - falsely concluding an effect exists when it does not. Such an error could lead research programs astray, wasting research funds and harming the scientist's academic reputation. To protect against Type I error, the level of statistical significance (α), its rate of occurrence in the frequentist paradigm of classical statistics, is usually set quite low, commonly at 0.05 (5%), or lower.
A Type II Error is the Failure to Reject a False Null Hypothesis
Environmental managers charged with monitoring environmental conditions may be more concerned about committing Type II errors - wrongly concluding an effect does not exist when in fact it does. Such an error could lead to over harvesting of a natural resource, or cause an increased risk to public health.
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