Rich Morris

Rich Morris

@sometimesdata.bsky.social

Senior research scientist in mental health and wellbeing, looking for an excuse to #rstats

185 followers419 following183 posts

I'm trying to find examples of replicable mistakes by Chat GPT. I seem to recall there was a thread on here about common causal inference mistakes made by AI. Does anyone have a tip? #causalsky

Some of the creators of Claude or other LLMs assign a greater than 0% chance the LLM is conscious. Something as high as 20 to 80%. This would be ethically problematic but I don't think this is right. Claude is non-committal on the topic but it also says it doesn't experience the passage of time.

“Every student learns that correlation does not imply causation. Few learn the converse: absence of correlation does not imply absence of causation. Both mistakes are common in applied work, and understanding why reveals something fundamental about what causal inference requires.”

koenfucius@koenfucius.bsky.social

“Correlation does *not* imply causation” is not the last word—it’s only the beginning. Cesar Chavez tracks how modern economics has been approaching causality (and the causality fallacy… and its reverse): bit.ly/4cpiLsm

protip for Gen Z working with older colleagues: if discussing data or studies which occurred entirely after the year 2000, you can refer to them as *21st Century*. It will sound futuristic and sci-fi to anyone who grew up on Buck Rogers, Flash Gordon (i.e., Gen X, Boomers) - very impressive!

Rich Morris reposted

Replying to @statsepi.bsky.social

We constantly empower people that have no interest in the truth whatsoever and I struggle to understand what can be done about that.

Add this to my reading list: “For “random intercept” models, adding the group-level averages of all included variables as regressors (or a variety of equivalent procedures such as group-wise centering) relieves the bias otherwise induced by this regularization”

Noah Greifer@noahgreifer.bsky.social

A paper I find extremely interesting on the equivalence between fixed effects models and "tweaked" random effects models: doi.org/10.1017/pan....

Spoke to Jen a few weeks ago because I am also trying to pivot out of academia. Can confirm Jen is a great person to help you focus on what’s important (to anyone else who is in a similar situation). She would be a great asset to any community building enterprise

Jen Richmond@jenrichmondphd.bsky.social

A bit of an update thread: I resigned from my academic position at UNSW a few months ago now and have been taking a break to rest, reflect, and work out what I want the next half of my career to look like. 1/9

Rich Morris reposted

After two weeks, I'm finally done! In this post, I explain different approaches for solving linear regression in R: directly, using QR, singular value and Cholesky decompositions, and do some benchmarking for comparison with in-built approaches. thomvolker.github.io/blog/2506_re...

Thom Volker@thomvolker.bsky.social

Having the time of my life writing a blog post about different ways to perform linear regression. It contains all the good stuff: a small piece on gradient descent, QR and singular value decompositions and R code.

Lord save me! Where do people get the idea the y-axis must always include zero, and every panel in a collection of panels must have the same y-axis scale? These a fine “rules of thumb” but they are not stone commandments handed down by the #dataviz gods!!! #rstats

One of my pet peeves with dplyr verbs in R is filter(). It should remove the rows which meet the condition(s). What is the most readable way to remove rows which meet a joint condition? e.g., remove rows where col_a == "x" & col_b > y? #rstats

One of my pet peeves with dplyr verbs in R is filter(). It should remove the rows which meet the condition(s). What is the most readable way to remove rows which meet a joint condition? e.g., remove rows where col_a == "x" & col_b > y? #rstats

TIL the difference between interaction and effect modification: "[An] interaction is the effect of 2 interventions whereas effect modification is the effect of one intervention varying across strata of a second variable." I think this means things like sex cannot interact with other variables...

Julia M. Rohrer@dingdingpeng.the100.ci

Thanks to everybody who chimed in! I arrived at the conclusion that (1) there's a lot of interesting stuff about interactions and (2) the figure I was looking for does not exist. So, I made it myself! Here's a simple illustration of how to control for confounding in interactions:>

stringr::str_wrap(width = ...) is fantastic! Works for facet headings as well: facet_wrap(~str_wrap(myfacet, width = 10)

Ben Harrap@bharrap.bsky.social

One of my favourite #ggplot tricks is wrapping long axis labels so they don't take up so much space on your chart Image 1 - Without wrapping Image 2 - With wrapping Image 3 - Code #rstats

In psychology, an operational definition specifies how a variable or concept is to be measured and defined within a particular study. How does this compare to a statistical estimand, which also defines what a study aims to estimate? I know they aren't the same but they seem quite similar #StatsSky

Rich Morris reposted

Well I feel like a fool. I’ve been saying NFTs and crypto serve no fundamental purpose. And I totally didn’t entertain the use case of bribing the US President. So embarrassing