p values are really quite nifty
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P -values are really quite nifty The P-Value Controversy: Where Do - PowerPoint PPT Presentation

P -values are really quite nifty The P-Value Controversy: Where Do We Go from Here? 2019 Joint Statistical Meetings Denver, Colorado Philip B. Stark July 2019 University of California, Berkeley 1 Whats a P -value? Suppose X is a random


  1. P -values are really quite nifty The P-Value Controversy: Where Do We Go from Here? 2019 Joint Statistical Meetings Denver, Colorado Philip B. Stark July 2019 University of California, Berkeley 1

  2. What’s a P -value? Suppose X is a random variable whose distribution is dominated by the uniform distribution if the null hypothesis is true: P 0 { X ≤ p } ≤ p for all p ∈ [0 , 1] . Then the observed value of X is a P -value. 2

  3. Where’s the problem? • Irrelevant/silly null. • Too abbreviated • Ignore design 3

  4. Where’s the problem? • Irrelevant/silly null. • Too abbreviated • Ignore design • P 0 { X ≤ p } > p . • Selective inference • Ignore design 3

  5. Where’s the problem? • Irrelevant/silly null. • Too abbreviated • Ignore design • P 0 { X ≤ p } > p . • Selective inference • Ignore design • Misinterpretation 3

  6. Where’s the problem? • Education and communication • Irrelevant/silly null. • Too abbreviated • “Disservice courses” • Ignore design • P 0 { X ≤ p } > p . • Selective inference • Ignore design • Misinterpretation 3

  7. Where’s the problem? • Education and communication • Irrelevant/silly null. • Too abbreviated • “Disservice courses” • Ignore design • Software makes it easy to commit • P 0 { X ≤ p } > p . silliness • Selective inference • Ignore design • Misinterpretation 3

  8. Where’s the problem? • Education and communication • Irrelevant/silly null. • Too abbreviated • “Disservice courses” • Ignore design • Software makes it easy to commit • P 0 { X ≤ p } > p . silliness • Selective inference • Cargo-Cult Statistics and • Ignore design Quantifauxcation • Misinterpretation • Editorial policies • Disciplinary practice & culture 3

  9. Why not just confidence intervals or confidence sets? Not all hypotheses are about parameters. E.g., 2-sample problem. 4

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