Type I Error Probability Measure
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A Type I Error Probability Measure is a hypothesis testing false positive statistical error probability measure that quantifies the predetermined probability of incorrectly rejecting a true null hypothesis in statistical hypothesis testing.
- AKA: Alpha Level, Significance Level, False Positive Rate, Type I Error Rate.
- Context:
- It can typically be set before Data Collection as a decision threshold for hypothesis rejection.
- It can typically control Long-Run Error Rate across multiple hypothesis tests.
- It can typically balance Statistical Conservatism against Statistical Power in test design.
- It can often determine Critical Values for test statistic evaluation.
- It can often influence Sample Size Requirements for achieving desired statistical power.
- It can often vary across Research Domains based on consequence severity of false positives.
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- It can range from being a Stringent Type I Error Probability Measure to being a Lenient Type I Error Probability Measure, depending on its threshold value.
- It can range from being a Fixed Type I Error Probability Measure to being an Adaptive Type I Error Probability Measure, depending on its adjustment strategy.
- It can range from being a Single-Test Type I Error Probability Measure to being a Family-Wise Type I Error Probability Measure, depending on its multiple testing scope.
- It can range from being a Traditional Type I Error Probability Measure to being a Adjusted Type I Error Probability Measure, depending on its correction method.
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- It can interact with Type II Error Probability through error rate tradeoffs.
- It can require Multiple Testing Correction in simultaneous hypothesis testing.
- It can inform Study Design Decisions through power analysis calculations.
- It can affect Publication Standards through significance threshold conventions.
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- Example(s):
- Standard Significance Levels, such as:
- 0.05 Type I Error Probability Measure representing 5% false positive risk (most common).
- 0.01 Type I Error Probability Measure representing 1% false positive risk (stringent).
- 0.10 Type I Error Probability Measure representing 10% false positive risk (exploratory).
- Domain-Specific Type I Error Probability Measures, such as:
- Particle Physics Discovery Level (5-sigma, approximately 3×10^-7) for new particle discovery.
- Clinical Trial Significance Level (often 0.025 for two-sided test).
- Genome-Wide Association Level (5×10^-8) for genetic association study.
- Adjusted Type I Error Probability Measures, such as:
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- Standard Significance Levels, such as:
- Counter-Example(s):
- P-Value, which is calculated from data rather than predetermined.
- Type II Error Probability, which represents false negative risk rather than false positive risk.
- Statistical Power, which represents probability of detecting true effect.
- Posterior Probability, which represents updated belief after observing data.
- See: Statistical Hypothesis Testing Task, Type I Hypothesis Testing Error, Significance Level, P-Value, Statistical Power, Multiple Testing Correction, False Positive Rate.