JASP markets itself as a user-friendly GUI for statistical analysis, but this ease of use comes at the cost of limited methodological depth and flexibility.

• The simplified interface encourages black-box application of statistics without fostering true understanding.
• Advanced users find the software restrictive, lacking support for custom models, complex data structures, and scripting.
• The default settings and automated procedures may lead novices to misuse or misinterpret results.

• While JASP supports basic meta-analysis, it lacks advanced capabilities such as network meta-analysis, multivariate models, and robust meta-regression.
• The Bayesian methods implemented are simplistic and do not cover the breadth needed for nuanced inference.
• Diagnostic tools for heterogeneity, publication bias, and influence analyses are basic or missing.

• JASP operates in isolation, with no built-in support for literature screening, data extraction, or risk of bias assessment.
• It offers no API or scripting interface, limiting reproducibility and workflow automation.
• Collaboration features are minimal or nonexistent.

• Although JASP allows export of analysis scripts, the lack of full scripting limits transparency compared to command-line alternatives.
• Version control and project management features are weak, hindering collaborative reproducible research.
• Output reports are standardized but offer limited customization.

• JASP’s low barrier to entry can foster overconfidence among inexperienced users, increasing risk of analytical errors.
• Professional statisticians and methodologists often reject JASP due to its limited scope and control.
• The software’s popularity in teaching may not translate to rigorous research environments.

JASP is a convenient tool for introductory statistics and teaching, but it is unsuitable for complex, high-stakes meta-analyses or advanced research. Its simplistic interface, limited features, and poor integration hinder rigorous evidence synthesis and reproducibility.

Recommendation: Use JASP for learning or exploratory data analysis only. For robust meta-analytic work, prefer more flexible and transparent tools like R packages or advanced workflow platforms.

• ✅ Full scripting flexibility for complex and customized meta-analyses
• ✅ Supports network meta-analysis, multivariate models, and Bayesian methods
• ✅ Integrates with R Markdown for reproducible research reports
• ➕ Why better than JASP:

Greater control, transparency, and methodological sophistication

• ✅ User-friendly GUI tailored to meta-analysis
• ✅ Supports subgroup and sensitivity analyses and other advanced features
• ✅ Widely used in clinical research with strong support
• ➕ Why better than JASP:

More focused and feature-rich for meta-analytic purposes

• ✅ Automate literature screening, data extraction, and bias assessment
• ✅ Reduce manual workload and increase accuracy
• ➕ Why better than JASP:

Streamlines upstream review tasks typically manual in JASP workflows

• ✅ Manage full systematic review workflow: screening, extraction, bias assessment, export
• ✅ Collaboration-friendly with version control and audit trails
• ➕ Why better than JASP:

Supports entire review lifecycle, not just statistical analysis

• Use R packages for advanced and reproducible meta-analyses.
• Use CMA for GUI-driven, feature-rich meta-analysis.
• Use Elicit and RobotReviewer to automate evidence extraction and bias assessment.
• Use Covidence or DistillerSR to manage the entire systematic review process.
• Use JASP primarily for teaching and simple exploratory analyses.