Type II Statistical Hypothesis Testing Error
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A Type II Statistical Hypothesis Testing Error is a statistical hypothesis testing decision error that occurs when a false null hypothesis fails to be rejected when the alternative hypothesis is actually true.
- AKA: Type II Error, False Negative Error, Beta Error, Missed Detection Error.
- Context:
- It can typically occur with probability β, where (1-β) represents the statistical power of the test.
- It can typically be reduced by increasing sample size or relaxing the significance level.
- It can often result from insufficient effect size relative to sample variability.
- It can often be more acceptable than Type I Statistical Hypothesis Testing Error in exploratory research.
- It can range from being a Low-Power Type II Statistical Hypothesis Testing Error to being a High-Power Type II Statistical Hypothesis Testing Error, depending on its test power.
- It can range from being a Small-Sample Type II Statistical Hypothesis Testing Error to being a Large-Sample Type II Statistical Hypothesis Testing Error, depending on its sample size.
- It can range from being a Weak-Effect Type II Statistical Hypothesis Testing Error to being a Strong-Effect Type II Statistical Hypothesis Testing Error, depending on its effect magnitude.
- It can range from being a Acceptable Type II Statistical Hypothesis Testing Error to being a Unacceptable Type II Statistical Hypothesis Testing Error, depending on its consequence severity.
- ...
- Example(s):
- Medical Diagnosis False Negatives, such as:
- A Disease Screening Miss that fails to detect actual disease.
- A Drug Side Effect Miss that overlooks harmful reactions.
- Quality Control Misses, such as:
- A Defect Detection Failure that passes defective products.
- A Process Shift Miss that fails to detect process degradation.
- Research Effect Misses, such as:
- An Underpowered Study Result with insufficient sample size.
- A Small Effect Size Miss below detection threshold.
- ...
- Medical Diagnosis False Negatives, such as:
- Counter-Example(s):
- A Type I Statistical Hypothesis Testing Error, which incorrectly rejects a true null hypothesis.
- A Correct Non-Rejection, which properly fails to reject a true null hypothesis.
- A Sampling Error, which affects sample representativeness rather than decision-making.
- See: Statistical Hypothesis Testing Decision Error, Type I Statistical Hypothesis Testing Error, Statistical Power, Sample Size Determination, Effect Size, Power Analysis, Beta Risk, Null Hypothesis, Alternative Hypothesis.