p-value

The Quantum Magician

by Derek Künsken  · 1 Oct 2018  · 430pp  · 107,765 words

distribution suggests to me that it has infected support systems.” “That’s not random,” Cassandra said. “No.” Cassandra had a brief urge to recalculate the p-value to verify the non-randomness, but Iekanjika wouldn’t care and Bel would already have calculated it. “The infection pattern doesn’t follow the systems

May Contain Lies: How Stories, Statistics, and Studies Exploit Our Biases—And What We Can Do About It

by Alex Edmans  · 13 May 2024  · 315pp  · 87,035 words

World Cup on stock markets – Alex Edmans and CNN’s Richard Quest’. Available at https://bit.ly/soccercnn 6. Data is Not Evidence: Causation * The ‘p-value’ corresponds to the significance level, which needs to be 0.05 or lower for a result to be deemed significant. † More technical terms for ‘common

Braiding Sweetgrass

by Robin Wall Kimmerer

-than-human world. I’ve never met an ecologist who came to the field for the love of data or for the wonder of a p-value. These are just ways we have of crossing the species boundary, of slipping off our human skin and wearing fins or feathers or foliage, trying

Thinking in Bets

by Annie Duke  · 6 Feb 2018  · 288pp  · 81,253 words

for others to assess the quality of the information being presented, systematized through peer review before publication. Confidence in the results is expressed through both p-values, the probability one would expect to get the result that was actually observed (akin to declaring your confidence on a scale of zero to ten

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football game, 56–59 Prisoner’s Dilemma (Poundstone), 19, 246n privacy, 157 Prospect Theory, 36 Prudential Retirement, 185 psychology, 145–47, 149 Pulitzer, Joseph, 60 p-values, 72 Rashomon, 157 Rashomon Effect, 157–58 rationality and irrationality, 11, 43, 51, 64, 181n, 183, 204 Ulysses contracts and, 201, 203 words, phrases, and

SciPy and NumPy

by Eli Bressert  · 14 Oct 2012  · 62pp  · 14,996 words

elements sample = np.random.randn(100) # normaltest tests the null hypothesis. out = stats.normaltest(sample) print('normaltest output') print('Z-score = ' + str(out[0])) print('P-value = ' + str(out[1])) # kstest is the Kolmogorov-Smirnov test for goodness of fit. # Here its sample is being tested against the normal distribution. # D is

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closer it is to 0 the better. out = stats.kstest(sample, 'norm') print('\nkstest output for the Normal distribution') print('D = ' + str(out[0])) print('P-value = ' + str(out[1])) # Similarly, this can be easily tested against other distributions, # like the Wald distribution. out = stats.kstest(sample, 'wald') print('\nkstest output for

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the Wald distribution') print('D = ' + str(out[0])) print('P-value = ' + str(out[1])) Researchers commonly use descriptive functions for statistics. Some descriptive functions that are available in the stats package include the geometric mean (gmean

The Unknowers: How Strategic Ignorance Rules the World

by Linsey McGoey  · 14 Sep 2019

Beautiful Testing: Leading Professionals Reveal How They Improve Software (Theory in Practice)

by Adam Goucher and Tim Riley  · 13 Oct 2009  · 351pp  · 123,876 words

being told, “Looks like the average of your generator is 7 when it should be 8,” than to being told, “I’m getting a small p-value from my Kolmogorov-Smirnov test.” Range Tests If a probability distribution has a limited range, the simplest thing to test is whether the output values

How to Diagnose and Fix Everything Electronic

by Michael Geier  · 6 Jan 2011  · 336pp  · 163,867 words

Why Information Grows: The Evolution of Order, From Atoms to Economies

by Cesar Hidalgo  · 1 Jun 2015  · 242pp  · 68,019 words

of the country’s population. 5. In the case of Honduras and Argentina the probability of the observed overlap (what is known academically as its p-value) is 4.4 × 10–4. The same probability is 2 × 10–2 for the overlap observed between Honduras and the Netherlands and 4 × 10–3

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics

by Thomas H. Davenport and Jinho Kim  · 10 Jun 2013  · 204pp  · 58,565 words

collected and tested to see how “unusual” it is under the temporary assumption that H0 is true. Rare or unusual data (often represented by a p-value below a specified threshold) is an indication that H0 is false, which constitutes a statistically significant result and support of the alternative hypothesis. Independent variable

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predictors would serve as independent variables. Alternative names are explanatory variable, predictor variable, and regressor. p-value: When performing a hypothesis test, the p-value gives the probability of data occurrence under the assumption that H0 is true. Small p-values are an indication of rare or unusual data from H0, which in turn provides support

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that H0 is actually false (and thus support of the alternative hypothesis). In hypothesis testing, we “reject the null hypothesis” when the p-value is less than the significance level a (Greek alpha), which is often 0.05 or 0.01. When the null hypothesis is rejected, the result

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(if H0 were indeed true) for us to doubt H0 and reject it as being true. In practice, this is often assessed by calculating a p-value; p-values less than alpha are indication that H0 is rejected and the alternative supported. t-test or student’s t-test: A test statistic that tests

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error: This error occurs when the null hypothesis is true, but it is rejected. In traditional hypothesis testing, one rejects the null hypothesis if the p-value is smaller than the significance level α. So, the probability of incorrectly rejecting a true null hypothesis equals α and thus this error is also

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reassuring to the wife but persuasive to her husband as well. In statistical hypothesis testing, the probability of 0.003 calculated above is called the p-value—the probability of obtaining a test statistic (e.g., Z-value of 2.75 in this case) at least as extreme as the one that

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hypothesis (H0) is “This baby is my husband’s.” In traditional hypothesis testing, one rejects the null hypothesis if the p-value is smaller than the significance level. In this case a p-value of 0.003 would result in the rejection of the null hypothesis even at the 1 percent significance level—typically

Science in the Soul: Selected Writings of a Passionate Rationalist

by Richard Dawkins  · 15 Mar 2017  · 420pp  · 130,714 words

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by Jeff Leek  · 1 Mar 2015  · 50pp  · 13,399 words

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by Michael Blastland  · 3 Apr 2019  · 290pp  · 82,871 words

Exploring Everyday Things with R and Ruby

by Sau Sheong Chang  · 27 Jun 2012

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by Conrad Barski  · 13 Nov 2014  · 273pp  · 72,024 words

The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling

by Adam Kucharski  · 23 Feb 2016  · 360pp  · 85,321 words

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

by Wes McKinney  · 25 Sep 2017  · 1,829pp  · 135,521 words

Numpy Beginner's Guide - Third Edition

by Ivan Idris  · 23 Jun 2015  · 681pp  · 64,159 words

Learn Algorithmic Trading

by Sebastien Donadio  · 7 Nov 2019

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by Aurelien Geron  · 14 Aug 2019

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by Glyn Moody  · 26 Sep 2022  · 295pp  · 66,912 words

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by Ben Grynhaus, Jordan Hudgens, Rayon Hunte, Matthew Thomas Morgan and Wekoslav Stefanovski  · 28 Jul 2021  · 739pp  · 174,990 words

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by Kaiser Fung  · 25 Jan 2010  · 227pp  · 62,177 words

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by Davis Edwards  · 10 Jul 2014

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by Nassim Nicholas Taleb  · 20 Feb 2018  · 306pp  · 82,765 words

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by Drew Conway and John Myles White  · 10 Feb 2012  · 451pp  · 103,606 words

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by Andrew W. Lo  · 3 Apr 2017  · 733pp  · 179,391 words

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by Christian Rudder  · 8 Sep 2014  · 366pp  · 76,476 words

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by Bruce Frey  · 9 May 2006  · 755pp  · 121,290 words

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by Francis Fukuyama  · 1 Mar 2000

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by Scott E. Page  · 27 Nov 2018  · 543pp  · 153,550 words

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

by Gregory Zuckerman  · 5 Nov 2019  · 407pp  · 104,622 words

How Medicine Works and When It Doesn't: Learning Who to Trust to Get and Stay Healthy

by F. Perry Wilson  · 24 Jan 2023  · 286pp  · 92,521 words

Human Diversity: The Biology of Gender, Race, and Class

by Charles Murray  · 28 Jan 2020  · 741pp  · 199,502 words

On the Edge: The Art of Risking Everything

by Nate Silver  · 12 Aug 2024  · 848pp  · 227,015 words

Everything Is Predictable: How Bayesian Statistics Explain Our World

by Tom Chivers  · 6 May 2024  · 283pp  · 102,484 words

Real World Haskell

by Bryan O'Sullivan, John Goerzen, Donald Stewart and Donald Bruce Stewart  · 2 Dec 2008  · 1,065pp  · 229,099 words

The Art of Computer Programming: Sorting and Searching

by Donald Ervin Knuth  · 15 Jan 1998

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

by Foster Provost and Tom Fawcett  · 30 Jun 2013  · 660pp  · 141,595 words

Debtor Nation: The History of America in Red Ink (Politics and Society in Modern America)

by Louis Hyman  · 3 Jan 2011

The Precipice: Existential Risk and the Future of Humanity

by Toby Ord  · 24 Mar 2020  · 513pp  · 152,381 words

Beginning R: The Statistical Programming Language

by Mark Gardener  · 13 Jun 2012

Topics in Market Microstructure

by Ilija I. Zovko  · 1 Nov 2008  · 119pp  · 10,356 words

Commodity Trading Advisors: Risk, Performance Analysis, and Selection

by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant and Fabrice Douglas Rouah  · 23 Sep 2004

NumPy Cookbook

by Ivan Idris  · 30 Sep 2012  · 197pp  · 35,256 words

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals

by David Aronson  · 1 Nov 2006

Unweaving the Rainbow

by Richard Dawkins  · 7 Aug 2011  · 339pp  · 112,979 words

Bad Science

by Ben Goldacre  · 1 Jan 2008  · 322pp  · 107,576 words

Market Risk Analysis, Quantitative Methods in Finance

by Carol Alexander  · 2 Jan 2007  · 320pp  · 33,385 words

Advances in Financial Machine Learning

by Marcos Lopez de Prado  · 2 Feb 2018  · 571pp  · 105,054 words

The Half-Life of Facts: Why Everything We Know Has an Expiration Date

by Samuel Arbesman  · 31 Aug 2012  · 284pp  · 79,265 words

Security Analysis

by Benjamin Graham and David Dodd  · 1 Jan 1962  · 1,042pp  · 266,547 words

God Created the Integers: The Mathematical Breakthroughs That Changed History

by Stephen Hawking  · 28 Mar 2007

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues

by Alain Ruttiens  · 24 Apr 2013  · 447pp  · 104,258 words

The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk

by William J. Bernstein  · 12 Oct 2000

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day

by John H. Johnson  · 27 Apr 2016  · 250pp  · 64,011 words

Monte Carlo Simulation and Finance

by Don L. McLeish  · 1 Apr 2005

The Art of Assembly Language

by Randall Hyde  · 8 Sep 2003  · 968pp  · 224,513 words

New Dark Age: Technology and the End of the Future

by James Bridle  · 18 Jun 2018  · 301pp  · 85,263 words

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006

by Ben Goertzel and Pei Wang  · 1 Jan 2007  · 303pp  · 67,891 words

Scikit-Learn Cookbook

by Trent Hauck  · 3 Nov 2014

Expected Returns: An Investor's Guide to Harvesting Market Rewards

by Antti Ilmanen  · 4 Apr 2011  · 1,088pp  · 228,743 words

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron  · 13 Mar 2017  · 1,331pp  · 163,200 words

Python for Finance

by Yuxing Yan  · 24 Apr 2014  · 408pp  · 85,118 words

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

by Irene Aldridge  · 1 Dec 2009  · 354pp  · 26,550 words

In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation

by William J. Cook  · 1 Jan 2011  · 245pp  · 12,162 words

Valuation: Measuring and Managing the Value of Companies

by Tim Koller, McKinsey, Company Inc., Marc Goedhart, David Wessels, Barbara Schwimmer and Franziska Manoury  · 16 Aug 2015  · 892pp  · 91,000 words

What’s Your Type?

by Merve Emre  · 16 Aug 2018  · 384pp  · 112,971 words

UNIX® Network Programming, Volume 1: The Sockets Networking API, 3rd Edition

by W. Richard Stevens, Bill Fenner, Andrew M. Rudoff  · 8 Jun 2013

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

by Michael Kearns and Aaron Roth  · 3 Oct 2019

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth

by Stuart Ritchie  · 20 Jul 2020

Doing Data Science: Straight Talk From the Frontline

by Cathy O'Neil and Rachel Schutt  · 8 Oct 2013  · 523pp  · 112,185 words

Quantitative Trading: How to Build Your Own Algorithmic Trading Business

by Ernie Chan  · 17 Nov 2008

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy

by Sharon Bertsch McGrayne  · 16 May 2011  · 561pp  · 120,899 words

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All

by Robert Elliott Smith  · 26 Jun 2019  · 370pp  · 107,983 words

Peer-to-Peer

by Andy Oram  · 26 Feb 2001  · 673pp  · 164,804 words

Network Security Through Data Analysis: Building Situational Awareness

by Michael S Collins  · 23 Feb 2014  · 446pp  · 102,421 words

The 4-Hour Body: An Uncommon Guide to Rapid Fat-Loss, Incredible Sex, and Becoming Superhuman

by Timothy Ferriss  · 1 Dec 2010  · 836pp  · 158,284 words

How to Read a Paper: The Basics of Evidence-Based Medicine

by Trisha Greenhalgh  · 18 Nov 2010  · 321pp  · 97,661 words

Scarcity: The True Cost of Not Having Enough

by Sendhil Mullainathan  · 3 Sep 2014  · 305pp  · 89,103 words

Super Thinking: The Big Book of Mental Models

by Gabriel Weinberg and Lauren McCann  · 17 Jun 2019

Not Working: Where Have All the Good Jobs Gone?

by David G. Blanchflower  · 12 Apr 2021  · 566pp  · 160,453 words

Spite: The Upside of Your Dark Side

by Simon McCarthy-Jones  · 12 Apr 2021

AIQ: How People and Machines Are Smarter Together

by Nick Polson and James Scott  · 14 May 2018  · 301pp  · 85,126 words

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives

by David Sumpter  · 18 Jun 2018  · 276pp  · 81,153 words

Advanced Software Testing—Vol. 3, 2nd Edition

by Jamie L. Mitchell and Rex Black  · 15 Feb 2015

JavaScript Cookbook

by Shelley Powers  · 23 Jul 2010  · 1,038pp  · 137,468 words

No Slack: The Financial Lives of Low-Income Americans

by Michael S. Barr  · 20 Mar 2012

R Cookbook

by Paul Teetor  · 28 Mar 2011  · 719pp  · 104,316 words

Mastering Pandas

by Femi Anthony  · 21 Jun 2015  · 589pp  · 69,193 words

Randomistas: How Radical Researchers Changed Our World

by Andrew Leigh  · 14 Sep 2018  · 340pp  · 94,464 words

Programming Ruby 1.9: The Pragmatic Programmer's Guide

by Dave Thomas, Chad Fowler and Andy Hunt  · 15 Dec 2000  · 936pp  · 85,745 words

Handbook of Modeling High-Frequency Data in Finance

by Frederi G. Viens, Maria C. Mariani and Ionut Florescu  · 20 Dec 2011  · 443pp  · 51,804 words

Black Edge: Inside Information, Dirty Money, and the Quest to Bring Down the Most Wanted Man on Wall Street

by Sheelah Kolhatkar  · 7 Feb 2017  · 385pp  · 118,901 words

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by Mike Cantelon, Marc Harter, Tj Holowaychuk and Nathan Rajlich  · 27 Jul 2013  · 628pp  · 107,927 words

Asset and Risk Management: Risk Oriented Finance

by Louis Esch, Robert Kieffer and Thierry Lopez  · 28 Nov 2005  · 416pp  · 39,022 words

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors

by Wesley R. Gray and Tobias E. Carlisle  · 29 Nov 2012  · 263pp  · 75,455 words

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by Trevor Hastie, Robert Tibshirani and Jerome Friedman  · 25 Aug 2009  · 764pp  · 261,694 words

Calling Bullshit: The Art of Scepticism in a Data-Driven World

by Jevin D. West and Carl T. Bergstrom  · 3 Aug 2020

The Art of Statistics: How to Learn From Data

by David Spiegelhalter  · 2 Sep 2019  · 404pp  · 92,713 words

Data Mining in Time Series Databases

by Mark Last, Abraham Kandel and Horst Bunke  · 24 Jun 2004  · 205pp  · 20,452 words

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by Ruey S. Tsay  · 14 Oct 2001

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures

by Frank J. Fabozzi  · 25 Feb 2008  · 923pp  · 163,556 words

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by Anthony T. Holdener  · 25 Jan 2008  · 982pp  · 221,145 words

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by Sarah Boslaugh  · 10 Nov 2012

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by Joel Grus  · 13 Apr 2015  · 579pp  · 76,657 words

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by David Beazley and Brian K. Jones  · 9 May 2013  · 1,606pp  · 168,061 words

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by Meghnad Desai  · 20 May 2013

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by John Abramson  · 20 Sep 2004  · 436pp  · 123,488 words

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by Lanius, Ruth A.; Vermetten, Eric; Pain, Clare  · 11 Jan 2011

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by David Spiegelhalter  · 14 Oct 2019  · 442pp  · 94,734 words

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by Brian Klaas  · 23 Jan 2024  · 250pp  · 96,870 words

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