What inspired this wish list request?
Currently, the Profiler in JMP displays confidence intervals (CI) and prediction intervals (PI) without indicating the confidence level that was used to generate these intervals (e.g., 90%, 95%). When reviewing analyses—especially those created by colleagues, older projects, or exported interactive HTML profiles—it is often unclear which probability level underlies the displayed intervals.
This lack of explicit information slows down interpretation and verification of results. For example, when comparing model outputs across studies or when validating reports, the user needs to confirm whether intervals are based on 90%, 95%, or another confidence level. Today this requires manual checking in the model settings or the CI/PI formula (name), which is error‑prone and not possible at all in exported HTML views.
What is the improvement you would like to see?
JMP should display the confidence level used for confidence and prediction intervals directly within the Profiler interface. This could be shown next to the interval type (e.g., “95% CI”), in the legend, or within the curve displays themselves. Additionally, when exporting interactive HTML profiles, this information should automatically be included so that users viewing the exported content immediately understand the basis of the intervals.
Optional enhancements could include:
A tooltip or label near the interval bands (e.g., “CI = 90%”).
A small annotation above/below the profiler axes.
An option in the Profiler settings to toggle interval probability display on/off.
This would bring the functionality in line with other statistical software where interval probability levels are visible by default.
Why is this idea important?
Displaying the interval probability dramatically increases transparency, reproducibility, and efficiency. Users can instantly understand the assumptions used to generate interval estimates without searching through menus or documentation.
This reduces misinterpretation risk, speeds up review cycles, facilitates documentation, and improves clarity in collaborative environments—especially when sharing results with non‑JMP users through exported HTML profiles.
Knowing whether uncertainty ranges reflect 90%, 95%, or another level is essential for correct decision‑making. Making this explicit enhances trust and usability and supports good statistical practice.
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