Learn how to distinguish truth from illusion that so often force investors

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1. Since childhood I have always thought of my future career.
All members of my family are professional musicians, but
I was (Differ)
2. I wanted to change the world by becoming a (Science)
3. I persuaded my parents to buy me some laboratory equipment
so that I could do some experiments at home. This was not
such a good idea!
4. (Fortunately)I wasn’t very good at science, and not at all skilful when it came to doing experiments.
5. I remember when I made a (Disaster) attempt to
create a new perfume for my friend. The mixture of ingredients
I used was wrong, because there was a small explosion
followed by a lot of smoke and a horrible smell.
6. After that I decided to study (Proper) to get the right
qualification to become a chemist.
7. I had a big (Argue) about it with my family
who still wanted me to study music – but I won in the end.

Truth or Truthiness: Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist

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This is not a book most will like. Without the correct priming Wainer can be difficult to understand. I read this as a supplement in a doctoral program to better understand how to think through problems from the angle of a statistician and researcher. Snooze fest sentence I know but dont let me lose you. We would regularly discuss Wainer and the studies he proposed. Even though brilliant, he packs in too much information for a casual read thru. Because of this, each chapter needs to be slowly This is not a book most will like. Without the correct priming Wainer can be difficult to understand. I read this as a supplement in a doctoral program to better understand how to think through problems from the angle of a statistician and researcher. Snooze fest sentence I know but don’t let me lose you. We would regularly discuss Wainer and the studies he proposed. Even though brilliant, he packs in too much information for a casual read thru. Because of this, each chapter needs to be slowly read and then reread. I learned a ton from this and will reread it as I am sure I missed a ton. The cover will make people think it’s simple.

This book is not for the casual reader or someone looking for a night-stand book. . more

Wainer’s stated purpose in writing this book is to help his readers develop habits of mind that allow them to distinguish truth from things that feel right but actually aren’t (truthiness). His annotated table of contents is very clear and well-organized, and he makes some very good points. He walks us through what kinds of experiments would be necessary to prove certain claims, talks about what we should then do if those experiments aren’t possible (they often aren’t), and concludes with some Wainer’s stated purpose in writing this book is to help his readers develop habits of mind that allow them to distinguish truth from things that feel right but actually aren’t (truthiness). His annotated table of contents is very clear and well-organized, and he makes some very good points. He walks us through what kinds of experiments would be necessary to prove certain claims, talks about what we should then do if those experiments aren’t possible (they often aren’t), and concludes with some very interesting examples, including the controversial teacher tenure and testing that are mentions in the book blurb.

Wainer worked for the Educational Testing Services for years, and his examples involving testing are enlightening and worth pulling out for discussion in and of themselves. A few of his case studies in the later chapters are pretty well-researched and take into account history and context in guessing at why the statistics say what they do. Really good stuff. I’m glad I stuck it out and got to them, because I found the opening chapters of the book, in which Wainer is laying out how to think correctly, a bit off-putting.

The book blurb talks about “his trademark verve and irreverence”, but mostly I would call his overall voice snarky. And sometimes that’s amusing. We especially like to let old men get away with it. If you’re in the mood to hear faux nostalgia for the good old days before Nate Silver when statisticians were uncool and he could be left in peace on an airline trip, this is the book for you. And he would love to show you his son’s Princeton acceptance letter — yep, printed in right in there, with a statistic on how rare they are just in case you didn’t know. He makes snide comments about cheaters, sure, but also about almost everyone else his stories run across. So you have to be in the mood to be amused by a curmudgeon. Also you have to let him get away with being dismissive of any guessing or reasoning in case studies in different directions that clearly don’t interest him much. I almost refrained from saying we’d never let a woman write like this, but there it sneaked in.

But if you can get past that, this is really a good outline for a few tests to put statistics through before you believe them. That’s a good tool to arm yourself with when venturing out into the internet. And a good chapter on what to look for in a graphic and how data can hide in plain site. He also ends with a chapter titled “Don’t try this at home”, which must be some kind of humor I don’t understand, because he very much encouraged us to try this at home and shares stories of people (strangely focusing on one very unusual family) who do investigate statistics with impressive results.

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All in all, I learned a lot, and that’s a great thing, but I was looking for something that I could unequivocally recommend to my science-major students, and I’m not sure this is it. But if you’re ready to sit back and hear what this character has to say and learn what you can from him, you won’t regret it.

I got a free copy of this from Net Galley. . more

“Truth or Truthiness” is about how to design better causal studies and better graphs. It’s mainly targeted at people working in education who can influence policies about testing, tenure, and such. It’s written in a very formal way and uses technical language. The author assumed the reader already knew what a “longitudinal study” and “cross-sectional study” are, for example, and that you understand words like “ancillary information,” “covariates,” and “legerdemain.” Some words were defined, but “Truth or Truthiness” is about how to design better causal studies and better graphs. It’s mainly targeted at people working in education who can influence policies about testing, tenure, and such. It’s written in a very formal way and uses technical language. The author assumed the reader already knew what a “longitudinal study” and “cross-sectional study” are, for example, and that you understand words like “ancillary information,” “covariates,” and “legerdemain.” Some words were defined, but often pages after the author first used them.

While the author was inspired by real claims or studies, many of his Case Studies used made up data to illustrate his point. He explained how to set up a random-assignment controlled experiment, which is the gold standard when possible. He then explained ways to increase the accuracy of observational studies, like gathering additional information when randomization is impossible and how to interpret the results while including missing data (from people dropping out of the study, dying, etc.). He showed how to use extrapolation, ways to deal with unexpected events, and how to create effective graphs to clearly present the information discovered in a study. He also did some ranting about current education policies (removing tenure, detecting cheating, measuring school performance, changes in the SAT, and the accuracy of subscores).

While the information about creating better studies seemed useful, I did not care for his mocking, dismissive tone. For example, he acknowledges that there may be a missing “third variable” in regards to fracking apparently causing increased earthquakes. However, since he can’t think of one, there must not be one. People who have pointed out (in their own way) that it’s not a certain cause-effect get mocked by the author. I happen to agree with him about fracking, but he mocks people for assuming things because “it makes sense to them.” Yet when he does it, his conclusions are based solely on “logic and evidence” and everyone else is either stupid or corrupt.

I received this book as a review copy from the publisher. . more

The book is a sort of mash up of epistemology, statistics, and general musings. I thought early on, it was going to be more tightly focussed on truth versus truthiness, then I thought it was going to be on the Don Rubin causal model, then I thought it was a manifesto for modern data science. It was a bit looser than this, but an enjoyable read nonetheless.

The author has a particular interest in education, and this informs many (but not all) the examples.

If there were themes tying this together, The book is a sort of mash up of epistemology, statistics, and general musings. I thought early on, it was going to be more tightly focussed on truth versus truthiness, then I thought it was going to be on the Don Rubin causal model, then I thought it was a manifesto for modern data science. It was a bit looser than this, but an enjoyable read nonetheless.

The author has a particular interest in education, and this informs many (but not all) the examples.

If there were themes tying this together, other than an epistemological bent to his statistics, it is the idea of always starting with an idealised experiment, then working back to reality, and a focus on clear thinking and communication.

I even had that deceptive sense of understanding the ‘counter factual’ while I was reading the book, although sadly that passed.

This isn’t bland. The author’s voice comes through clearly. He has a great knack for quotes and turns of phrase that support his interests.

“Dear God, make my enemies ridiculous” Voltaire.
“I will listen to any hypothesis, but on one condition – that you show me a method by which it can be tested.” von Hofman

Its another one that gets a worthy spot on the bookshelf, and would do with a bit more note-taking. It’s not so technical that the interested generalist would necessarily struggle through.

BTW truth in the book’s case is belief backed up by evidence. Truthiness is when you believe something based purely on the feels. He has a bit of fun with this. . more

Learn how to distinguish truth from illusion that so often force investors

ONLINE ENGLISH GRAMMAR QUIZ
topic: Gerunds 2 | level: Intermediate

Fill in each space with either the gerund (“-ing”) OR the infinitive (to . ) form of the verb.

Example: “Reading in the dark is difficult.” OR “Don’t forget to brush your teeth.”

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