A few months ago, we announced that LabPlot had received funding from the NGI Zero Core fund. This was to help us focus on three features we’ve wanted to add for a while: Analysis of Live Data, Python scripting, and more statistical analysis functions.

We’re pleased to announce that we have now completed the main goals for this project. As part of the new statistical functions, we’ve added a comprehensive suite of statistical hypothesis tests:

  • One-Sample t-Test
  • Independent Two-Sample t-Test
  • Paired Two-Sample t-Test
  • Welch t-Test
  • One-Way ANOVA Test
  • One-Way ANOVA with Repeated Measures Test
  • Mann-Whitney U Test
  • Wilcoxon Signed Rank Test
  • Kruskal-Wallis Test
  • Friedman Test
  • Log-Rank Test
  • Chi-Square Independence Test
  • Chi-Square Goodness of Fit Test

These new features have been implemented and will be ready for you to use soon. We hope they will be a valuable addition for our users. This work was made possible by the financial support from the NLnet Foundation and the European Commission through the Next Generation Internet Program, and we are grateful for their contribution.

The work on LabPlot continues, and our team is already busy on the next set of improvements and functions. As always, your feedback is important in guiding our next steps. We look forward to sharing more updates with you in the future.