Category: Statistics

Cool tennis-tracking app

Swupnil Sahai writes that he’s developed Swing, “the best app for tracking all of your tennis stats, and maybe we’ll expand to other sports in the future.” According to Swupnil, the app runs on Apple Watch making predictions in real time. I hope in the future they’ll incorporate some hierarchical modeling to deal with sparse-data […]

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Cool tennis-tracking app

Swupnil Sahai writes that he’s developed Swing, “the best app for tracking all of your tennis stats, and maybe we’ll expand to other sports in the future.” According to Swupnil, the app runs on Apple Watch making predictions in real time. I hope in the future they’ll incorporate some hierarchical modeling to deal with sparse-data […]

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It should be ok to just publish the data.

Gur Huberman asked for my reaction to a recent manuscript, Are CEOs Different? Characteristics of Top Managers, by Steven Kaplan and Morten Sorensen. The paper begins: We use a dataset of over 2,600 executive assessments to study thirty individual characteristics of candidates for top executive positions – CEO, CFO, COO and others. We classify the […]

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It should be ok to just publish the data.

Gur Huberman asked for my reaction to a recent manuscript, Are CEOs Different? Characteristics of Top Managers, by Steven Kaplan and Morten Sorensen. The paper begins: We use a dataset of over 2,600 executive assessments to study thirty individual characteristics of candidates for top executive positions – CEO, CFO, COO and others. We classify the […]

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The Law and Order of Data Science

One conversation I’ve had a few times revolves around the question, “What’s the difference between science and data science?” If I were to come up with a simple distinction, I might say that

Science starts with a question; data science starts with …

It was the weeds that bothered him.

Bill Jefferys points to this news article by Denise Grady. Bill noticed the following bit, “In male rats, the studies linked tumors in the heart to high exposure to radiation from the phones. But that problem did not occur in female rats, or any mice,” and asked: ​Forking paths, much? My reply: The summary of […]

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It was the weeds that bothered him.

Bill Jefferys points to this news article by Denise Grady. Bill noticed the following bit, “In male rats, the studies linked tumors in the heart to high exposure to radiation from the phones. But that problem did not occur in female rats, or any mice,” and asked: ​Forking paths, much? My reply: The summary of […]

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A. Spanos: Egon Pearson’s Neglected Contributions to Statistics

Continuing with the discussion of E.S. Pearson in honor of his birthday: Egon Pearson’s Neglected Contributions to Statistics by Aris Spanos     Egon Pearson (11 August 1895 – 12 June 1980), is widely known today for his contribution in recasting of Fisher’s significance testing into the Neyman-Pearson (1933) theory of hypothesis testing. Occasionally, he is also […]

How feminism has made me a better scientist

Feminism is not a branch of science. It is not a set of testable propositions about the observable world, nor is it any single research method. From my own perspective, feminism is a political movement associated with successes such as votes for women, setbacks such as the failed Equal Rights Amendment, and continuing struggles in […]

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How feminism has made me a better scientist

Feminism is not a branch of science. It is not a set of testable propositions about the observable world, nor is it any single research method. From my own perspective, feminism is a political movement associated with successes such as votes for women, setbacks such as the failed Equal Rights Amendment, and continuing struggles in […]

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“Usefully skeptical science journalism”

Dean Eckles writes: I like this Wired piece on the challenges of learning about how technologies are affecting us and children. The journalist introducing a nice analogy (that he had in mind before talking with me — I’m briefly quoted) between the challenges in nutrition (and observational epidemiology more generally) and in studying “addictive” technologies. […]

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“Usefully skeptical science journalism”

Dean Eckles writes: I like this Wired piece on the challenges of learning about how technologies are affecting us and children. The journalist introducing a nice analogy (that he had in mind before talking with me — I’m briefly quoted) between the challenges in nutrition (and observational epidemiology more generally) and in studying “addictive” technologies. […]

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Egon Pearson’s Heresy

Today is Egon Pearson’s birthday. In honor of his birthday, I am posting “Statistical Concepts in Their Relation to Reality” (Pearson 1955). I’ve posted it several times over the years, but always find a new gem or two, despite its being so short. E. Pearson rejected some of the familiar tenets that have come to […]

Discussion of the value of a mathematical model for the dissemination of propaganda

A couple people pointed me to this article, “How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,” by James Weatherall, Cailin O’Connor, and Justin Bruner, also featured in this news article. Their paper begins: In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe […]

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Discussion of the value of a mathematical model for the dissemination of propaganda

A couple people pointed me to this article, “How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,” by James Weatherall, Cailin O’Connor, and Justin Bruner, also featured in this news article. Their paper begins: In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe […]

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Jeremy Freese was ahead of the curve

Here’s sociologist Jeremy Freese writing, back in 2008: Key findings in quantitative social science are often interaction effects in which the estimated “effect” of a continuous variable on an outcome for one group is found to differ from the estimated effect for another group. An example I use when teaching is that the relationship between […]

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Jeremy Freese was ahead of the curve

Here’s sociologist Jeremy Freese writing, back in 2008: Key findings in quantitative social science are often interaction effects in which the estimated “effect” of a continuous variable on an outcome for one group is found to differ from the estimated effect for another group. An example I use when teaching is that the relationship between […]

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What’s gonna happen in the 2018 midterm elections?

Following up on yesterday’s post on party balancing, here’s a new article from Joe Bafumi, Bob Erikson, and Chris Wlezien giving their predictions for November: We forecast party control of the US House of Representatives after the 2018 midterm election. First, we model the expected national vote relying on available generic Congressional polls and the […]

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What’s gonna happen in the 2018 midterm elections?

Following up on yesterday’s post on party balancing, here’s a new article from Joe Bafumi, Bob Erikson, and Chris Wlezien giving their predictions for November: We forecast party control of the US House of Representatives after the 2018 midterm election. First, we model the expected national vote relying on available generic Congressional polls and the […]

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Some thoughts after reading “Bad Blood: Secrets and Lies in a Silicon Valley Startup”

I just read the above-titled John Carreyrou book, and it’s as excellent as everyone says it is. I suppose it’s the mark of any compelling story that it will bring to mind other things you’ve been thinking about, and in this case I saw many connections between the story of Theranos—a company that raised billions […]

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