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Reposted by František Bartoš

JASP Services @jasp-services.com · 8d

New blog post! Why companies win by investing in open-source software. With zebra-painted cows, of course.

www.jasp-services.com/why-companie...

Why Companies Win by Investing in Open-Source Software - JASP Services BV

In 2025, the IgNobel prize in Biology went to research on “whether cows painted with zebra-like striping can avoid being bitten by flies“. Suppose a fictitious company, ZebraCows, wishes to exploit th...

www.jasp-services.com

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Reposted by František Bartoš

Suyog Chandramouli @suyoghc.bsky.social · 8d

We are pleased to have
@fbartos.bsky.social
join us today, Tuesday, September 30th, 11am (EST) to talk about Bayesian Hypothesis testing! This is followed by a workshop on using JASP for statistics around 12:10pm. The zoom is open to public with details in the flyer!
@PsychPrinceton

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Reposted by František Bartoš

Richard D. Morey @richarddmorey.bsky.social · 13d

Simonsohn has now posted a blog response to our recent paper about the poor statistical properties of the P curve. @clintin.bsky.social and I are finishing up a less-technical paper that will serve as a response. But I wanted to address a meta-issue *around* this that may clarify some things. 1/x

Data Colada @datacolada.bsky.social · 14d

Would p-curve work if you dropped a piano on it?
datacolada.org/129

PIano being dropped on car in car testing facility

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František Bartoš @fbartos.bsky.social · 14d

> Why are you actively misrepresenting what others are saying all the time?

I'm happy to discuss with you in person if we meet anywhere, but I don't find replying to you online very productive at this point.

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František Bartoš @fbartos.bsky.social · 14d

> Carter et al are right, and you are wrong

That's pretty much just arguing from authority

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František Bartoš @fbartos.bsky.social · 14d

I did not say meta-analyses with huge heterogeneity lol. I said under any heterogeneity. Would you consider tau = 0.1-0.2 on Cohen's d scale with an average effect size of 0.2-0.4 huge? I would not. Pretty meaningful result (and probably representative of many meta-analyses), but p-curve fails.

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František Bartoš @fbartos.bsky.social · 14d

> P-curve does what worse than random effects?

All the simulations I linked shows that p-curve estimates the effect size worse, on averate, than random effects.

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František Bartoš @fbartos.bsky.social · 14d

Must've been a bug on the platform -- I could not see any responses I sent to the thread but other features worked fine.

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František Bartoš @fbartos.bsky.social · 14d

For some reason, I cannot reply to Lakens anymore?

Regardless, if anyone is interested in the topic:
- Carter does not say something completely opposite to my claims
- I^2 is not a measure of absolute heterogeneity, Laken's argument strawmans meta-analysis
- p-curve does worse than random effects

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František Bartoš @fbartos.bsky.social · 14d

It's not completely opposed - they say that they work well only under no heterogeneity. From their and other simulation studies it seems like that a simple random effects model performs better than p-curve even when publication bias is present. As such, I don't see any reason for using the method.

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František Bartoš @fbartos.bsky.social · 14d

How is it directly opposite to what I'm saying?

Also, glad we got to the late-stage science when you start pulling arguments of authority. Always great debating with you :)

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František Bartoš @fbartos.bsky.social · 14d

You are still free to find any third-party realistic simulations to address my claim :)

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František Bartoš @fbartos.bsky.social · 14d

The issue is it fails even with low heterogeneity; you are just caricaturing any other slightly heterogeneous meta-analysis right now.

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František Bartoš @fbartos.bsky.social · 14d

> And hey, even if a paper they wrote in 2014 on a new method is now partially outdated, so what?

I accept the critique and acknowledge the method is outdated and should not be used. It might have been a great idea back then but it did not turn out to be any more.

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Reposted by František Bartoš

JASP @jaspstats.bsky.social · 14d

Sage created a 44-second promo video for our book "Discovering Statistics Using JASP". Blog post: jasp-stats.org/2025/09/24/d...

The video itself: www.youtube.com/watch?v=MP0N...

"Discovering Statistics Using JASP": The Promo Video - JASP - Free and User-Friendly Statistical Software

The shiny new textbook “Discovering Statistics Using JASP” now has a 44-second promo video on YouTube, courtesy of Sage. What I like about the video is that it shows some of the pages, so you get an o...

jasp-stats.org

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František Bartoš @fbartos.bsky.social · 14d

Can anyone point me to the simulation studies showing that p-curve performs well under realistic conditions? And any done by someone else than pcurve authors? As far as I know, p-curve fails horrendously as long as any heterogeneity is involved...

doi.org/10.1177/1745...
doi.org/10.1002/jrsm...

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František Bartoš @fbartos.bsky.social · 21d

btw, we just released JASP 0.95.2, which fixes some previously reported stability issues -- consider updating your version :)

František Bartoš @fbartos.bsky.social · 21d

The Bayesian part also provides more guidance on specifying prior distributions for estimation, testing, and model-averaging (with different effect sizes and in different settings).

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František Bartoš @fbartos.bsky.social · 21d

Each manuscript walks you through three examples describing the applications of different meta-analytic tools, including

- effect size calculation
- funnel plot
- forest plot
- bubble plot
- simple meta-analysis
- meta-regression
- multilevel and multivariate models.

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František Bartoš @fbartos.bsky.social · 21d

We released two preprints describing the JASP Meta-Analytic functionality in detail.

Meta-Analysis with JASP, Part I: Classical Approaches (arxiv.org/abs/2509.09845)

Meta-Analysis with JASP, Part II: Bayesian Approaches (arxiv.org/abs/2509.09850)

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František Bartoš @fbartos.bsky.social · 28d

The methodology is implemented in the RoBMA R package
(update is coming to CRAN soon).

Vignette demonstrating the analyses:
fbartos.github.io/RoBMA/articl...

Preprint:
doi.org/10.48550/arX...

Z-Curve Publication Bias Diagnostics

fbartos.github.io

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František Bartoš @fbartos.bsky.social · 28d

We derive posterior predictive distributions for many meta-analytic models. Importantly, meta-analytic models that ignore these discontinuities misfit the data and should not be used for inference; models that respect them provide a better basis for inference.

(see a couple of examples attached)

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František Bartoš @fbartos.bsky.social · 28d

Publication bias is usually indicated by sharp discontinuities—typically at the significance threshold (selection for significance) or at zero (selection for positive results).

Similar plots are often used in metaresearch, we bring them to meta-analysis!

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František Bartoš @fbartos.bsky.social · 28d

Z-curve plot is a new visual model fit diagnostic for #metaanalysis with an emphasis on #publicationbias. In contrast to funnel plots, z-curve plots
- visualize the distribution of z-statistics (where bias usually occurs)
- compare the fit of multiple models simultaneously

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František Bartoš @fbartos.bsky.social · 28d

Publication bias is usually indicated by sharp discontinuities—typically at the significance threshold (selection for significance) or at zero (selection for positive results).

Similar plots are often used in metaresearch, we bring them to meta-analysis!

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