Statistics | Nick Byrd, Ph.D.

Upon Reflection, Ep. 10: Great Minds Do Not Think Alike

This time I read my 2022 paper in Review of Philosophy and Psychology titled, “Great Minds Do Not Think Alike: Philosophers’ Views Predicted by Reflection, Education, Personality, and Other Demographic Differences“. As the title suggests, various psychological factors predicted variance in philosophers’ answers to classic philosophical questions. This raises questions about how psychological and demographic differences can explain philosophical differences. There are also implications for scientific psychologists as well as academic philosophers.

Podcast: Play in new window | Download (Duration: 47:14 — 86.5MB)

Two Years In The Life Of A Grad Student: Time Logging Data

I have had some side gigs in graduate school that involved creating invoices for hourly work—web development, copyediting, research assistance, etc. I used Toggl to log my time. At some point, I realized that I could log all of my work time—not just the billable time. So in 2018 and 2019, I logged all of my work time. In this post, I will summarize the 2018 and 2019 data and mention some take-aways for 2020.

New Talk: Great Minds Do Not Think Alike

I’ll be presenting new data from a pre-registered replication at some conferences in the next few months. The study replicated findings that those with a Ph.D. in philosophy are more reflective, that less reflective philosophers tended towards certain philosophical views, and that some of these reflection-philosophy correlations are partly confoudned with culture, education, gender, or personality.

March 2022 update: these data are in a paper that was accepted by Review of Philosophy & Psychology.

The meaning of ‘statistical significance’ and of p-values

A 2019 paper in the Advances in Methods and Practices in Psychological Science found that most psychology textbooks, instructors, and students misinterpret ‘statistical significance’ and p-values. Talk about a headline! More important than the headline, however, are the right interpretations and what we can do to correct widespread misinterpretations. In this post, I explain the authors’ findings and the three solutions they propose.

Upon Reflection Podcast Ep. 1: What We Can Infer About Implicit Bias

Welcome to the first episode of Upon Reflection, a podcast about what we think as well as how and why we think it.

A screenshot of the first page of the paper "What We Can (And Can't) Infer About Implicit Bias From Debiasing Experiments".

In this podcast, I’ll be reading my paper entitled, “What We Can (And Can’t) Infer About Implicit Bias From Debiasing Experiments“. I argue that implicit bias is not entirely unconscious or involuntary, but it probably is associative. As with all of my papers, the free preprint of the paper can be found on my CV at byrdnick.com/cv under “Publications“.

If this sounds like the kind of research that you want to hear more about, you can subscribe to Upon Reflection wherever you find podcasts. You can also find out more about me and my research on Twitter via @byrd_nick, or on Facebook via @byrdnick. If you end up enjoying the Upon Reflection podcast, then feel free to tell people about it, online, in person, or in your ⭐️⭐️⭐️⭐️⭐️ review.

Podcast: Play in new window | Download (Duration: 53:23 — 48.9MB)

4 Free DIY Data Analysis & Statistics Tools

One of the questions I get a lot these days is, “How can I learn data analysis without actually taking a statistics course?” In this post, I will relay my answers to that question.

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New Paper: What We Can (And Can’t) Infer About Implicit Bias From Debiasing Experiments

Synthese has just published one of my papers on implicit bias. As with all of my papers, you can find a link to the free preprint on my CV: byrdnick.com/cv. The final, corrected, and typeset version is on Synthese’s website and the audio version is on my podcast. In this post, you will find a non-technical overview of the paper’s main point and then the TLDR explainer.

Continue reading New Paper: What We Can (And Can’t) Infer About Implicit Bias From Debiasing Experiments

University and Department Rankings: A Custom Solution

Lots of people pay close attention to the US News National University Rankings. But those rankings assume all users have the same priorities. Moreover, some people want field-specific rankings that compare universities at the department level (e.g., the Philosophy department at Harvard vs. the Philosophy department at MIT). Ranking-obsessed philosophers have had the Philosophical Gourmet Report to rank philosophy Ph.D. programs since at least 1996—1989 if you count the pre-internet version. For many reasons, academic philosophers are becoming more vocal about their criticism of these philosophy rankings (e.g., Bruya 2015, De Cruz ~~2016~~ 2018). In this post, I will propose a (new?) custom ranking system. This system will address common complaints about philosophy’s existing ranking system: a custom ranking system will be more versatile, up-to-date, and generalizable.

1. THE COMPLAINTS

The complaints about the rankings are voluminous — what else would you expect from philosophers? In lieu of an outline of every blog post and every public statement, I provide a list of major themes that fall into three different categories: the practice of ranking, the current process of ranking, and the current leadership of the ranking.

Complaints About Ranking

Rankings might misrepresent the magnitude of the differences between departments.
Rankings might indicate a false sense of hierarchy and/or prestige.
Ordinal lists just aren’t that informative.

Complaints About Process

Continue reading University and Department Rankings: A Custom Solution

Category: Statistics