• Complain

Aubrey Clayton - Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science

Here you can read online Aubrey Clayton - Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2021, publisher: Columbia University Press, genre: Children. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Aubrey Clayton Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science
  • Book:
    Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science
  • Author:
  • Publisher:
    Columbia University Press
  • Genre:
  • Year:
    2021
  • Rating:
    4 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 80
    • 1
    • 2
    • 3
    • 4
    • 5

Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

There is a logical flaw in the statistical methods used across experimental science. This fault is not a minor academic quibble: it underlies a reproducibility crisis now threatening entire disciplines. In an increasingly statistics-reliant society, this same deeply rooted error shapes decisions in medicine, law, and public policy with profound consequences. The foundation of the problem is a misunderstanding of probability and its role in making inferences from observations.Aubrey Clayton traces the history of how statistics went astray, beginning with the groundbreaking work of the seventeenth-century mathematician Jacob Bernoulli and winding through gambling, astronomy, and genetics. Clayton recounts the feuds among rival schools of statistics, exploring the surprisingly human problems that gave rise to the discipline and the all-too-human shortcomings that derailed it. He highlights how influential nineteenth- and twentieth-century figures developed a statistical methodology they claimed was purely objective in order to silence critics of their political agendas, including eugenics.Clayton provides a clear account of the mathematics and logic of probability, conveying complex concepts accessibly for readers interested in the statistical methods that frame our understanding of the world. He contends that we need to take a Bayesian approachthat is, to incorporate prior knowledge when reasoning with incomplete informationin order to resolve the crisis. Ranging across math, philosophy, and culture, Bernoullis Fallacy explains why something has gone wrong with how we use dataand how to fix it.

Aubrey Clayton: author's other books


Who wrote Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science? Find out the surname, the name of the author of the book and a list of all author's works by series.

Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
Table of Contents
Bernoullis Fallacy Statistical Illogic and the Crisis of Modern Science - image 1
BERNOULLIS
FALLACY
Bernoullis Fallacy Statistical Illogic and the Crisis of Modern Science - image 2
BERNOULLIS
FALLACY
Bernoullis Fallacy Statistical Illogic and the Crisis of Modern Science - image 3
Statistical Illogic
and the Crisis of
Modern Science
AUBREY CLAYTON
COLUMBIA UNIVERSITY PRESS
NEW YORK
Columbia University Press gratefully acknowledges the generous support for this - photo 4
Columbia University Press gratefully acknowledges the generous support for this book provided by a member of the Publishers Circle.
Picture 5
Columbia University Press
Publishers Since 1893
New YorkChichester, West Sussex
cup.columbia.edu
Copyright 2021 Aubrey Clayton
All rights reserved
EISBN 978-0-231-55335-3
Library of Congress Cataloging-in-Publication Data
Names: Clayton, Aubrey, author.
Title: Bernoulli's fallacy : statistical illogic and the crisis of modern
science / Aubrey Clayton.
Description: New York : Columbia University Press, 2021. | Includes
bibliographical references and index.
Identifiers: LCCN 2021004250 (print) | LCCN 2021004251 (ebook) | ISBN
9780231199940 (hardback) | ISBN 9780231553353 (ebook)
Subjects: LCSH: Bernoulli, Jakob, 16541705Influence. |
ProbabilitiesPhilosophy19th century. |
ProbabilitiesPhilosophy20th century. | Mathematical
statisticsPhilosophy. | Binomial distribution. | Law of large numbers.
Classification: LCC QA273.A35 C53 2021 (print) | LCC QA273.A35 (ebook) |
DDC 519.2dc23
LC record available at https://lccn.loc.gov/2021004250
LC ebook record available at https://lccn.loc.gov/2021004251
A Columbia University Press E-book.
CUP would be pleased to hear about your reading experience with this e-book at .
Cover design: Noah Arlow
Disclaimer: The views expressed are solely those of the author and do not reflect the views of
his employer, Moody's Analytics, or its parent company, Moody's Corporation, or its affiliates.
Dedicated to Jameel Al-Aidroos, of blessed memory.
CONTENTS
S ince this book risks being accused of relitigating old arguments about statistics and science, let us first dispense with the idea that those arguments were ever settled. The statistics wars never ended; in some ways they have only just begun.
Science, statistics, and philosophy need each other now as much as ever, especially in the context of the still unfolding crisis of replication. Everyone regardless of ideology can likely agree that something is wrong with the practice of statistics in science. Now is also the right time for a frank conversation because statistical language is increasingly a part of our daily communal lives. The COVID-19 pandemic has, sadly, forced statistical terms like test sensitivity, specificity, and positive predictive value into our collective lexicon. Meanwhile, in other recent examples, (spurious) statistical arguments were a core component of the allegations of electoral fraud in the 2020 U.S. presidential elections, and (non-spurious) statistical arguments are central to the allegations of systemic racial bias in the U.S. criminal justice system. The largest stories of our timein public health, education, government, civil rights, the environment, business, and many other domainsare being told using the rhetorical devices of statistics. So the recognition that statistical rhetoric might lend itself to misuse makes this an urgent problem with an ethical dimension. On that we can probably also agree.
What to do about it is another matter. In science, several proposed methodological changes (discussed in the following) have gained support as potential solutions to the replication crisis, but there are no clear winners yet. The reason consensus is hard to come by is that there are unresolved foundational questions of statistics lurking within these debates about methods. The discussions happening now can, in fact, be seen as a vibrant remixing of the same philosophical issues that have colored the controversies about statistics since the 1800s. In short, assessing whether a proposed change successfully fixes a problem requires one to first decide what the problems are, and such decisions reveal philosophical commitments about the process by which scientific knowledge is created. When it comes to such foundational questions, we are not all on the same page, for reasons explored in this book.
Because statistical methods are a means of accounting for the epistemic role of measurement error and uncertainty, the statistics wars (at least on the frequentist versus Bayesian front) are best described as a dispute about the nature and origins of probability: whether it comes from outside us in the form of uncontrollable random noise in observations, or inside us as our uncertainty given limited information on the state of the world. The first perspective limits the scope of probability to those kinds of chance fluctuations we can, in principle, tabulate empirically; the second one allows for probability to reflect a degree of confidence in a hypothesis, both before and after some new observations are considered. Unfortunately for the conflict-averse, there is no neutral position here.
As a snapshot of the ways these philosophical commitments are now playing themselves out in practice, consider that much of the current debate about statistical and scientific methods can be organized into three categories of concerns:
Where does the hypothesis come from and when? If a particular hypothesis, representing a concrete prediction of the ways a research theory will be borne out in some measured variables, is crafted after peeking at the results or going on a fishing expedition to find a version that best suits the available data, then it may be considered a suspicious product of post hoc theorizing, also known as hypothesizing after results are known (HARKing), taking advantage of researcher degrees of freedom, the Texas sharpshooter fallacy, data dredging, the look-elsewhere effect, or p-hacking. Various proposals to combat this include the pre-registration of methods, that is, committing to a certain rigid process of interpreting the data before it has been gathered, sequestering the exploratory phase of research from the confirmatory one, or correcting for multiple possible comparisons, as in the Bonferroni correction (dividing the threshold for significance by the number of simultaneous hypotheses being considered) or others like it.
What caused the experiment to begin and end, and how did we come to learn about it? Subcategories of this concern include the problem of publication bias, or the file drawer problem, and the problem of optional stopping. If, say, an experimenter conducting a trial is allowed to keep running the experiment and collecting data until a favorable result is obtained and only then report that result, there is apparently the potential for malfeasance. Attempts to block this kind of behavior include making publication decisions solely on the basis of the pre-registered reportsthat is, based purely on the methodsto encourage publishing negative results, and for the stopping rule to be explicitly specified ahead of time and adhered to.
Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science»

Look at similar books to Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science»

Discussion, reviews of the book Bernoullis Fallacy: Statistical Illogic and the Crisis of Modern Science and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.