Im unsure whether I understand the data structure you propose. You can, e.g., visualize the same unit across different conditions. Consider below: a line for each flower.
You could do a line for each animal across 5 slices (conditions) on the x-axis?
Does this (partially) answer your question?

Finally, here is the link to the open access article:
doi.org/10.3758/s134...

I agree: Stacking can be beneficial. Here is one way how that might look like in JASP (Figure 4 from the article).

Thanks for the fun project!
@donvdbergh.bsky.social, @fbartos.bsky.social, @njudd.com, @jordyvanlangen.bsky.social, @lukekorthals.bsky.social, @rogierk.bsky.social, @ejwagenmakers.bsky.social

The next step in empirical sciences towards transparent data visualization is the widespread adoption of raincloud plots through accessible, free software.

🧵👇

Whether its academia, industry, or education: Raincloud plots offer a more complete report of your data as they simultaneously show summary statistics, a density estimate, & individual data points.

With that, they beat prevalent bar charts.

🧵👇

just found Cordyceps Hills and Palms on your website,
very nice!

2. Prior predictive checks. As I simulated data for my conditions (beyond assigning a prior for effect size), I got a feeling for what struck me as (un-)reasonable.
Here is a great tutorial: link.springer.com/article/10.3...

Two things helped me:

1. I browsed the literature for effects that were somewhat related and I derived a plausible/reasonable prior. Ie what are the papers closest to your novel effect? Yes, they might have done (remotely) different things, yet offer a ballpark.

2. 👇