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Topic:
Inference errors and practical significance
Multiple Testing and False Positives
Describing Errors in Context
Balancing Errors When Designing a Study
Alpha as the Type I Error Rate in t Tests
Why Huge Samples Make Tiny Differences Significant
Power and Type II Error Probability
How Effect Size and Variability Affect Power
Evaluating Claims Based on Significant Results
Why Small Samples Miss Real Differences
What Power of a Test Means
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