It’s a balmy day today on Ship StatInfasST: An invigorating wind has a salutary effect on our journey. So, for the first time I’m excerpting all of Excursion 5 Tour I (proofs) of Statistical Inference as Severe Testing How to Get Beyond the Statistics Wars (2018, CUP) A salutary effect of power analysis is that […]
Category: Statistical Inference as Severe Testing
Blurbs of 16 Tours: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST)
Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP) Deborah G. Mayo Abstract for Book By disinterring the underlying statistical philosophies this book sets the stage for understanding and finally getting beyond today’s most pressing controversies revolving around statistical methods and irreproducible findings. Statistical Inference as Severe Testing takes the reader on […]
Excerpt from Excursion 4 Tour IV: More Auditing: Objectivity and Model Checking
4.8 All Models Are False . . . it does not seem helpful just to say that all models are wrong. The very word model implies simplification and idealization. . . . The construction of idealized representations that capture important stable aspects of such systems is, however, a vital part of general scientific analysis. (Cox 1995, p. 456) […]
Mementos from Excursion 4: Objectivity & Auditing: Blurbs of Tours I – IV
Excursion 4: Objectivity and Auditing (blurbs of Tours I – IV) Excursion 4 Tour I: The Myth of “The Myth of Objectivity” Blanket slogans such as “all methods are equally objective and subjective” trivialize into oblivion the problem of objectivity. Such cavalier attitudes are at odds with the moves to take back science […]
Excerpt from Excursion 4 Tour II: 4.4 “Do P-Values Exaggerate the Evidence?”
Excerpt from Excursion 4 Tour II* 4.4 Do P-Values Exaggerate the Evidence? “Significance levels overstate the evidence against the null hypothesis,” is a line you may often hear. Your first question is: What do you mean by overstating the evidence against a hypothesis? Several (honest) answers are possible. Here is one possibility: What I […]
severe testing : beyond Statistics wars?!
A timely start to my reading Deborah Mayo’s [properly printed] Statistical Inference as Severe Testing (How to get beyond the Statistics Wars) on the Armistice Day, as it seems to call for just this, an armistice! And the opportunity of a long flight to Oaxaca in addition… However, this was only the start and it […]
January Invites: Ask me questions (about SIST), Write Discussion Analyses (U-Phils)
ASK ME. Some readers say they’re not sure where to ask a question of comprehension on Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP)–SIST– so here’s a special post to park your questions of comprehension (to be placed in the comments) on a little over the first half of […]
SIST* Blog Posts: Excerpts & Mementos (to Dec 31 2018)
Excerpts 05/19: The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars 09/08: Excursion 1 Tour I: Beyond Probabilism and Performance: Severity Requirement (1.1) 09/11: Excursion 1 Tour I (2nd stop): Probabilism, Performance, and Probativeness (1.2) 09/15: Excursion 1 Tour I (3rd stop): The Current State of Play in Statistical Foundations: […]
60 Years of Cox’s (1958) Chestnut: Excerpt from Excursion 3 Tour II (Mayo 2018, CUP)
2018 marked 60 years since the famous weighing machine example from Sir David Cox (1958)[1]. It’s one of the “chestnuts” in the exhibits of “chestnuts and howlers” in Excursion 3 (Tour II) of my new book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST). It’s especially relevant to take this up now, just before […]
Excerpt from Excursion 4 Tour I: The Myth of “The Myth of Objectivity” (Mayo 2018, CUP)
Tour I The Myth of “The Myth of Objectivity”* Objectivity in statistics, as in science more generally, is a matter of both aims and methods. Objective science, in our view, aims to find out what is the case as regards aspects of the world [that hold] independently of our beliefs, biases and interests; thus […]
Tour Guide Mementos From Excursion 3 Tour III: Capability and Severity: Deeper Concepts
Excursion 3 Tour III: A long-standing family feud among frequentists is between hypotheses tests and confidence intervals (CIs). In fact there’s a clear duality between the two: the parameter values within the (1 – α) CI are those that are not rejectable by the corresponding test at level α. (3.7) illuminates both CIs and severity […]
Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III
Tour III Capability and Severity: Deeper Concepts From the itinerary: A long-standing family feud among frequentists is between hypotheses tests and confidence intervals (CIs), but in fact there’s a clear duality between the two. The dual mission of the first stop (Section 3.7) of this tour is to illuminate both CIs and severity by […]
Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III
Tour III Capability and Severity: Deeper Concepts From the itinerary: A long-standing family feud among frequentists is between hypotheses tests and confidence intervals (CIs), but in fact there’s a clear duality between the two. The dual mission of the first stop (Section 3.7) of this tour is to illuminate both CIs and severity by […]
Mementos for Excursion 3 Tour II (3.4-3.6): It’s the Methods, Stupid!
Excursion 3 Tour II: It’s The Methods, Stupid Tour II disentangles a jungle of conceptual issues at the heart of today’s statistics wars. (3.4) unearths the basis for a number of howlers and chestnuts thought to be licensed by Fisherian or N-P tests.* In each exhibit, we study the […]
It’s the Methods, Stupid: Excerpt from Excursion 3 Tour II (Mayo 2018, CUP)
Tour II It’s the Methods, Stupid There is perhaps in current literature a tendency to speak of the Neyman–Pearson contributions as some static system, rather than as part of the historical process of development of thought on statistical theory which is and will always go on. (Pearson 1962, 276) This goes for Fisherian contributions as […]
Memento & Quiz (on SEV): Excursion 3, Tour I.
As you enjoy the weekend discussion & concert in the Captain’s Central Limit Library & Lounge, your Tour Guide has prepared a brief overview of Excursion 3 Tour I, and a short (non-severe) quiz on severity, based on exhibit (i).* We move from Popper through a gallery on “Data Analysis in the 1919 Eclipse tests […]
First Look at N-P Methods as Severe Tests: Water plant accident [Exhibit (i) from Excursion 3]
Exhibit (i) N-P Methods as Severe Tests: First Look (Water Plant Accident) There’s been an accident at a water plant where our ship is docked, and the cooling system had to be repaired. It is meant to ensure that the mean temperature of discharged water stays below the temperature that threatens the ecosystem, perhaps not […]
Neyman-Pearson Tests: An Episode in Anglo-Polish Collaboration: Excerpt from Excursion 1 (3.2)
3.2 N-P Tests: An Episode in Anglo-Polish Collaboration* We proceed by setting up a specific hypothesis to test, H0 in Neyman’s and my terminology, the null hypothesis in R. A. Fisher’s . . . in choosing the test, we take into account alternatives to H0 which we believe possible or at any rate consider it most important to be […]
Where Are Fisher, Neyman, Pearson in 1919? Opening of Excursion 3
Excursion 3 Statistical Tests and Scientific Inference Tour I Ingenious and Severe Tests [T]he impressive thing about [the 1919 tests of Einstein’s theory of gravity] is the risk involved in a prediction of this kind. If observation shows that the predicted effect is definitely absent, then the theory is simply refuted.The theory is incompatible with […]
Stephen Senn: On the level. Why block structure matters and its relevance to Lord’s paradox (Guest Post)
Stephen Senn Consultant Statistician Edinburgh Introduction In a previous post I considered Lord’s paradox from the perspective of the ‘Rothamsted School’ and its approach to the analysis of experiments. I now illustrate this in some detail giving an example. What I shall do I have simulated data from an experiment in which two diets have […]