3 YEARS AGO (JANUARY 2014): MEMORY LANE

3 years ago...

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: January 2014. I mark in red three posts from each month that seem most apt for general background on key issues in this blog, excluding those reblogged recently[1], and in green up to 3 others I’d recommend[2].  Posts that are part of a “unit” or a group count as one. This month, I’m grouping the 3 posts from my seminar with A. Spanos, counting them as 1.

January 2014

  • (1/2) Winner of the December 2013 Palindrome Book Contest (Rejected Post)
  • (1/3) Error Statistics Philosophy: 2013
  • (1/4) Your 2014 wishing well. …
  • (1/7) “Philosophy of Statistical Inference and Modeling” New Course: Spring 2014: Mayo and Spanos: (Virginia Tech)
  • (1/11) Two Severities? (PhilSci and PhilStat)
  • (1/14) Statistical Science meets Philosophy of Science: blog beginnings
  • (1/16) Objective/subjective, dirty hands and all that: Gelman/Wasserman blogolog (ii)
  • (1/18) Sir Harold Jeffreys’ (tail area) one-liner: Sat night comedy [draft ii]
  • (1/22) Phil6334: “Philosophy of Statistical Inference and Modeling” New Course: Spring 2014: Mayo and Spanos (Virginia Tech) UPDATE: JAN 21
  • (1/24) Phil 6334: Slides from Day #1: Four Waves in Philosophy of Statistics
  • (1/25) U-Phil (Phil 6334) How should “prior information” enter in statistical inference?
  • (1/27) Winner of the January 2014 palindrome contest (rejected post)
  • (1/29) BOSTON COLLOQUIUM FOR PHILOSOPHY OF SCIENCE: Revisiting the Foundations of Statistics

    .

  • (1/31) Phil 6334: Day #2 Slides

 

[1] Monthly memory lanes began at the blog’s 3-year anniversary in Sept, 2014.

[2] New Rule, July 30, 2016-very convenient.

 

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Categories: 3-year memory lane, Bayesian/frequentist, Statistics | 1 Comment

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One thought on “3 YEARS AGO (JANUARY 2014): MEMORY LANE

  1. Jeffreys on p-values.

    Suppose the hypothesis is that the data follow the $N(0,1)$
    distribution. What observable results does this hypothesis predict? It
    seem pointless to predict a single value as such a prediction would be
    wrong with probability 1. The prediction must be a set ${\mathcal S}$
    of values with the prediction being regarded as correct if the
    observable result $x$ lies in ${\mathcal S}$. Putting ${\mathcal
    S}=\rz$ results in the prediction being correct with probability one
    but this is somewhat vacuous. A non-vacuous prediction can be obtained
    by specifying a probability $\alpha$ and a set ${\mathcal S}(\alpha)$
    such that the prediction is correct with probability $\alpha$, $\pr(X\in
    {\mathcal S}(\alpha))=\alpha$. It is worthy of note that the larger
    $\alpha$ the more vacuous the prediction so to speak. As a simple example put
    $\alpha=0.95$ and ${\mathcal S}(\alpha)=(-1.96,1.96)$ and suppose that
    $x=3.121$ is observed. The $p$-value is $\pr(\vert X \vert > 3.121)=
    0.0018$ and for this to be a successful prediction would require
    $\alpha=0.9982$ rather than the chosen $\alpha=0.95$. We now interpret
    `not predicted to occur’ in the sense `predicted not to
    occur’ rather than in the sense `forgetting to predict’. If it were
    agreed beforehand that a false prediction would lead to the null
    hypothesis to be rejected, then this is done because a value predicted
    not to occur, namely 3.121, did in fact occur. This seems an
    unremarkable procedure. How bad the prediction error is can be
    measured by the $\alpha=0.9982$ required to make the prediction
    correct and which corresponds to a very weak prediction in that it
    would be correct in 99.8\% of the times.

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