How did you pick where to eat the last time you had a craving for tacos? In the popular Netflix series Master of None, Dev Shah, a 30-year-old actor living in New York City, models one extreme approach: After deciding to get tacos with his friend Arnold, who opts for an “I’m good with whatever” approach, Dev spends 45 minutes frantically and meticulously searching the Internet for the best taco spot in New York. Dev finally selects a particular taco truck as the best option; upon arriving there, he grills the server about the most superior taco offered, only to discover that the taco truck is all out of tortillas. “What am I supposed to do now—go and eat the second-best taco?” Dev fumes.
If "spiritual intelligence" is a real thing, what might it consist of? Probably, elements of personality, intelligence, and altered states of consciousness.
One of the most exciting things to happen during the years-long debate about the replicability of psychological research is the shift in focus from providing evidence that there is a problem to developing concrete plans for solving those problems. Whether it is journal badges that reward good practices, statistical software that can check for problems before papers are published, collaborative efforts to deal with limited resources and underpowered studies, proposals for new standards of evidence, or even entire societies dedicated to figuring out what we can do to make thing better, many people have devoted an incredible amount of thought, time, and energy to figuring out how we can fix any problems that exist and move the field forward.
"Spiritual intelligence" has been popularized in recent years as an "alternative" intelligence based on little evidence, However, could the concept have some scientific merit?
The following is a guest post by Neil Lewis, Jr. Neil is an assistant professor at Cornell University.
Last week I visited the Center for Open Science
in Charlottesville, Virginia to participate in the second annual meeting
of the Society for the Improvement of Psychological Science
(SIPS). It was my first time going to SIPS, and I didn’t really know what to expect. The structure was unlike any other conference I’ve been to—it had very little formal structure—there were a few talks and workshops here and there, but the vast majority of the time was devoted to “hackathons
” and “unconference
” sessions where people got together and worked on addressing pressing issues in the field: making journals more transparent, designing syllabi for research methods courses, forming a new journal, changing departmental/university culture to reward open science practices, making open science more diverse and inclusive, and much more. Participants were free to work on whatever issues we wanted to and to set our own goals, timelines, and strategies for achieving those goals.
I spent most of the first two days at the diversity and inclusion hackathon
that Sanjay and I co-organized. These sessions blew me away. Maybe we’re a little cynical, but going into the conference we thought maybe two or three people would stop by and thus it would essentially be the two of us trying to figure out what to do to make open science more diverse and inclusive. Instead, we had almost 40 people come and spend the first day identifying barriers to diversity and inclusion, and developing tools to address those barriers. We had sub-teams working on (1) improving measurement of diversity statistics (hard to know how much of a diversity problem one has if there’s poor measurement), (2) figuring out methods to assist those who study hard-to-reach populations, (3) articulating the benefits of open science and resources to get started for those who are new, (4) leveraging social media for mentorship on open science practices, and (5) developing materials to help PIs and institutions more broadly recruit and
retain traditionally underrepresented students/scholars. Although we’re not finished, each team made substantial headway in each of these areas.
Last week was the second meeting of the Society for the Improvement of Psychological Science
, a.k.a. SIPS. SIPS is a service organization with the mission
of advancing and supporting all of psychological science. About 200 people met in Charlottesville, VA to participate in hackathons and lightning talks and unconference sessions, go to workshops, and meet other people interested in working to improve psychology.
What Is This Thing Called SIPS?
If you missed SIPS and are wondering what happened – or even if you were there but want to know more about the things you missed – here are a few resources I have found helpful:
The conference program
gives you an overview and the conference OSF page
has links to most of what went on, though it’s admittedly a lot to dig through. For an easier starting point, Richie Lennie posted an email
he wrote to his department with highlights and links, written specifically with non-attendees in mind.
Drilling down one level from the conference OSF page, all of the workshop presenters put their materials online
. I didn’t make it to any workshops so I appreciate having access to those resources. Continue reading
I’ve been suffering an acute bout of cognitive dissonance lately, finding myself disagreeing with people I admire, specifically, several of the authors of this article
. (The article has 72 authors and I don’t know all of them!) The gist of the article can be stated very simply and in the authors’ own words: “We propose to change the default P-value threshold for statistical significance for claims of new discoveries from .05 to .005.” This proposal is soberly, clearly argued and the article makes some good points, the best of which is that, imperfect as this change would be, at least it’s a step in the right direction. But I respectfully disagree. Here’s why.
I’m starting to think that p-levels should all be labeled “for entertainment purposes only.” They give a very very rough idea of the non-randomness of your data, and are kind of interesting to look at. So they’re not completely useless, but they are imprecise at best and almost impossible to interpret at worst*, and so should be treated as only one among many considerations when we decide what we as scientists actually believe. Other considerations (partial list): prior probabilities (also very rough!), conceptual coherence, consistency with related findings, and (hats off please) replicability. Continue reading