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JMP Wish List

We want to hear your ideas for improving JMP. Share them here.
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Obtain accuracy profile during analytical method validation

What inspired this wish list request? 

The current version of JMP available to us does not allow performing an Accuracy Profile (β‑expectation tolerance interval), which is a key statistical approach used in analytical method validation. As a result, it is currently difficult or not possible to properly assess total error by combining bias and precision in a single, regulatory‑accepted framework.

Example use case:
During validation of a quantitative analytical method (e.g. HPLC or bioanalytical assay), multiple measurements are generated across several concentration levels and analytical runs. Regulatory guidelines (such as SFSTP, EMA, or ICH Q2) recommend the use of the Accuracy Profile to demonstrate that the method is valid across the intended concentration range. With the current JMP version, these calculations and visualizations cannot be generated directly, requiring manual calculations or external tools, which are time‑consuming and prone to error.

 

What is the improvement you would like to see? 

I would like to have access to a newer version of JMP that includes the built‑in Accuracy Profile platform, allowing users to:

  • Compute β‑expectation tolerance intervals
  • Visualize bias and uncertainty against acceptance limits
  • Model intra‑ and inter‑series variability
  • Automatically generate validation‑ready graphs and statistics

 

Why is this idea important? 

This improvement is important because it directly supports regulatory compliance, data integrity, and efficiency.

Value for users:

  • Enables compliance with current analytical validation guidelines
  • Reduces the risk of calculation or transcription errors
  • Saves significant time compared with manual processing
  • Improves confidence during audits and inspections

Overall, having access to the Accuracy Profile in JMP is a must‑have capability for users involved in analytical method development and validation.