Are We Kissing GraphPad Prism Goodbye? Here's Everything you need to know.
Everything you need to know about GraphPad Prism vs. Tidy Plots
For years, GraphPad Prism has been the undisputed king of biological data visualization.
Easy to use. Pretty graphs. Built-in stats. Journals love it.
Wildtype One even wrote "The Biology Researcher's Guide to Choosing Correct Statistical Tests" based on Prism.
But Tidy Plots—a code-based R framework built on ggplot2—is creeping into the spotlight.
Why are postdocs and data-savvy PhDs leaving Prism? And should you?
Wildtype One will make the decision easier for you in 4 minutes (or less).
Here's everything you need to know about GraphPad Prism vs. Tidy Plots.
What makes Prism good
Prism dominated for so long because of its:
Point-and-click simplicity
Built-in stats (no need to remember what a Tukey post-hoc test is)
Clean, publication-ready figures
Fast enough for grant prep, lab meetings, and exploratory analysis
For small datasets and tight deadlines, Prism just works.
But biology changed.
Tidy Plots was built for big, messy data
If Prism is a Swiss Army knife, Tidy Plots is a full lab automation system.
Built on R + ggplot2, Tidy Plots lets you:
Script your visuals (so changes take seconds, not hours)
Batch-process hundreds of plots in one go
Integrate seamlessly with your stats pipeline
Handle real-world datasets (missing values, repeated measures, nested designs)
Reproducibility is where Tidy Plots shines
In Prism, it's easy to:
Drag the wrong data into a chart
Forget which condition was which
Copy-paste the wrong result into your figure
With Tidy Plots, your data, stats, and visuals are all connected by code.
That means:
No manual errors
No copy-paste mess
Full audit trail of every figure tweak
More journals and funders are asking for reproducibility. Tidy Plots builds that in from the start.
The part nobody says out loud
This isn’t Prism vs. Tidy.
It’s closed vs. open. Static vs. dynamic. Manual vs. automated.
Prism lives in a world of drag-and-drop, local files, and single-user workflows.
Tidy Plots lives in a world of:
GitHub repos
Version control
Scripted analyses
Team collaboration
Preprint-ready transparency
If you’re moving toward open science—or working on big, collaborative projects—Tidy Plots feels like the future.
So… should you ditch Prism?
Not necessarily. There is room for both in research. Just know the differences and what you lack.
Tidy Plots is better for:
Complex, repeated visuals
Reproducibility & automation
Visuals for preprints/open science
Team science, code-first workflows
But Prism still crushes for:
Quick analyses at lab meetings
Simple comparisons
Making clean plots when time is tight
Final thought
Biology is getting bigger, messier, and more data-rich by the year.
Prism made sense for the last decade.
Tidy Plots is built for what’s next.
So if you're still manually replotting qPCR data for the third time this week, maybe it’s time to ask:
"Is it me… or is it the tool?"
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