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.Continue reading Upon Reflection, Ep. 10: Great Minds Do Not Think Alike
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.Continue reading Two Years In The Life Of A Grad Student: Time Logging Data
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.
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.Continue reading The meaning of ‘statistical significance’ and of p-values
Welcome to the first episode of Upon Reflection, a podcast about what we think as well as how and why we think it.
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.
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.
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.