Truth or Truthiness Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist 1st Edition by Howard Wainer 9781316490754 1316490750

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ISBN 10: 1316490750
ISBN 13: 9781316490754
Author: Howard Wainer

Teacher tenure is a problem. Teacher tenure is a solution. Fracking is safe. Fracking causes earthquakes. Our kids are over-tested. Our kids are not tested enough. We read claims like these in the newspaper every day, often with no justification other than ‘it feels right’. How can we figure out what is right? Escaping from the clutches of truthiness begins with one simple question: ‘what is the evidence?’ With his usual verve and flair, Howard Wainer shows how the sceptical mindset of a data scientist can expose truthiness, nonsense, and outright deception. Using the tools of causal inference he evaluates the evidence, or lack thereof, supporting claims in many fields, with special emphasis in education. This wise book is a must-read for anyone who has ever wanted to challenge the pronouncements of authority figures and a lucid and captivating narrative that entertains and educates at the same time.

Truth or Truthiness Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist 1st Table of contents:

Section I Thinking Like a Data Scientist

1 How the Rule of 72 Can Provide Guidance to Advance Your Wealth, Your Career, and Your Gas Mileage

2 Piano Virtuosos and the Four-Minute Mile

3 Happiness and Causal Inference

Introduction

Happiness: Its Causes and Consequences

Conclusions

4 Causal Inference and Death

The Magic of Statistics Cannot Put Actual Numbers Where There Are None

Coronary Bypass Surgery: An Illuminating Example

Summary

Conclusion

5 Using Experiments to Answer Four Vexing Questions

Introduction

My Dream

On the Role of Experiments in Answering Causal Questions

Problem 1: Accommodation for Examinees with Disabilities

Problem 2: Unplanned Interruptions in Testing

Problem 3: Measuring the Effect of Feedback to Judges in Standard Setting

Problem 4: What Is the Value of “Teaching to the Test”?

Discussion and Conclusions

6 Causal Inferences from Observational Studies: Fracking, Injection Wells, Earthquakes, and Oklahoma

Introduction

Dewatering

Fracking

Concerns

A Possible Experiment to Study the Seismic Effects of Fracking

One Consequence of Not Having Good Estimates of the Causal Effect

An Observational Study

A Trout in the Milk

Conclusions

7 Life Follows Art

Section II Communicating Like a Data Scientist

8 On the Crucial Role of Empathy in the Design of Communications

9 Improving Data Displays

Introduction

Conclusion

10 Inside Out Plots

A Multivariate Example: Joe Mauer vs. Some Immortals

11 A Century and a Half of Moral Statistics

Joseph Fletcher and Maps of Moral Statistics

Guns, Murders, Life, Death, and Ignorance in Contemporary America

Variables and Data Sources

Claims about the Relation between Variables

Geographic Claim

Conclusion

Section III Applying the Tools of Data Science to Education

12 Waiting for Achilles

13 How Much Is Tenure Worth?

14 Detecting Cheating Badly

Introduction

Fishing for Cheaters

Industry Standards

Why Are These Standards Important?

False Positives and Mammograms

How Accurate Is the Cheater Detection Program in Question?

Coda

15 When Nothing Is Not Zero

16 Musing about Changes in the SAT

No Penalty for Guessing

Reduce the Amount of Arcane Vocabulary

Making the Writing Portion Optional

Coda

17 For Want of a Nail

A Clarifying Example – the U.S. Census

Back to Tests

If Not for Subscores, Is There Another Justification for Long Tests?

The Costs of Using Excessively Long Tests and the Progress of Western Civilization

Conclusions

One Caveat

Section IV Conclusion: Don’t Try This at Home

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