Imagine you’re planning a psychology study. You have a cool idea, but how can you know if your study design is strong before you collect any data? This is where Bayesian tools come in. These tools help you check how your assumptions (called priors) might affect your results. They also help you see if your study will have enough power to detect effects.
TL;DR
Before collecting data, psychologists use “Bayesian Power and Prior-Sensitivity Checker Tools” to make sure their studies are worth the effort. These tools help spot weak spots in study designs and highlight when results could change too much depending on the priors chosen. Using them leads to better, more reliable research. Here are six tools you should know if you’re getting started with Bayesian analysis in psychology.
1. BayesPlay – Interactive and Easy on the Eyes
BayesPlay is perfect for those new to Bayesian stats. It uses a fun, drag-and-drop interface. You can simulate different experimental scenarios, play with prior beliefs, and instantly see how results change.
- No coding needed!
- Supports common test types: t-tests, correlations, linear regression
- Includes both power and sensitivity analyses
It’s great for teaching and learning. Plus, it’s browser-based, so no downloads.
Best for: Beginners, students, and visual learners.
2. BayesFactor Design Analysis (BFDA) by Schönbrodt
BFDA is a brilliant R package created by Felix Schönbrodt. It’s built for researchers who use Bayes Factors to evaluate evidence. BFDA helps decide how large your sample size should be to get meaningful Bayes Factors.
This tool is perfect when you want:
- Bayes Factors that support the null hypothesis
- To avoid collecting more data than needed
- To set priors in a thoughtful way
Requires some R coding skills, but worth the time!
Best for: Intermediate users, researchers looking for precise Bayes Factor outcomes.
3. JASP – The All-in-One Wonder Tool
JASP is popular among psychologists for traditional stats. But it also supports Bayesian modeling! With its user-friendly layout, JASP lets you adjust priors, rerun models, and easily visualize how different priors affect your conclusions.
Its Bayesian tools include:
- Bayesian t-tests
- Linear regression
- ANOVA
And the best part? It auto-generates beautiful plots and clear results. No coding needed.
Best for: Users switching from traditional stats to Bayesian approaches.
4. bayestestR – Priors Made Transparent
bayestestR is a handy R package that simplifies working with priors. It allows you to explore how different priors shape your posterior distributions. You can easily simulate different scenarios and compare the effect of “skeptical” vs “optimistic” priors.
Its strengths include:
- Works with models from other packages like brms and rstanarm
- Great tools for interpreting intervals and ROPEs (Regions of Practical Equivalence)
- Visualizes sensitivity nicely
Use this tool if you’re serious about understanding and justifying your priors.
Best for: Advanced users doing complex model checking.
5. BAPT – Bayesian Power for Precise Planning
Bayesian Analysis of Power and Type I error (BAPT) is a tool that aims at power analysis but through the Bayesian lens. Unlike traditional power calculators, BAPT lets you see the probability that a future Bayes Factor will favor your hypothesis.
It includes options like:
- Custom priors
- One or two-sided tests
- Visual feedback on power based on different outcomes
BAPT is available as an R package and as a Shiny app, making it easier to try.
Best for: Planning studies that use Bayes Factors as the main inference tool.
6. SimDesign – Build Custom Simulation Studies
If none of the above tools fit your needs perfectly, you might love SimDesign. This powerful R package lets you build your own simulation frameworks from scratch. Want to design a custom psychology experiment with Bayesian stats and multiple priors? SimDesign has your back.
Use it for:
- Custom diagnostics
- Advanced power simulations
- Extreme flexibility in design features
It’s a tool for those who dream in code. But it gives you total control over your simulation design.
Best for: Power users and data scientists in psychology.
Why Should Psychologists Care About This?
Running a psychology study isn’t free. Participants, time, and resources all cost something. If you’re building your study on shaky assumptions, you might waste everything. That’s why checking prior sensitivity and Bayesian power before collecting data is a smart move.
Here’s what can go wrong when you skip these checks:
- You overestimate an effect — and miss the truth
- You choose priors that bias your results
- You gather too little or too much data
These tools help you prevent those mistakes before they happen.
Keys to Picking the Right Tool
Here’s a quick matchmaking list to help you choose:
| Tool | Use Case | Skill Level |
|---|---|---|
| BayesPlay | Interactive simulations & visuals | Beginner |
| BFDA | Bayes Factor design analysis | Intermediate to Advanced |
| JASP | All-in-one stats platform | Beginner to Intermediate |
| bayestestR | Sensitivity to priors | Advanced |
| BAPT | Planning with Bayes Factor probabilities | Intermediate |
| SimDesign | Fully customizable simulations | Advanced |
Final Thoughts
Psychologists do incredible work exploring the human mind. But good studies don’t happen by accident. By using Bayesian power and prior-sensitivity tools before collecting data, researchers make sure their efforts lead to strong, reliable findings.
Pick the tool that suits your style and level. Whether it’s the drag-and-drop joys of BayesPlay or the power-packed precision of SimDesign, these tools can help your good ideas shine before any data is gathered.
And remember: Better planning means better science!
