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Statistics at California Institute of Technology

Statistical Inference Flashcards, Generated by AI

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Statistical Inference

What is the difference between Type I and Type II errors?

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ANSWER

Type I error (false positive, α): rejecting a true null hypothesis — concluding there's an effect when there isn't one. Type II error (false negative, β): failing to reject a false null hypothesis — missing a real effect. Power = 1 − β. Lowering α increases β and vice versa. The significance level (typically 0.05) sets the acceptable Type I error rate.

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Spaced repetition for Statistics
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