tl;dr – balance v0.12.0

We're excited to announce balance v0.12.0! Since our initial release, balance has evolved into a comprehensive Python package for adjusting biased samples. This post highlights the most significant improvements from v0.1.0 (2022-11-20) through v0.12.0 (2025-10-14), showcasing how we've made balance easier to use:

• Expanded compatibility: Now supports Python 3.9–3.14 on Windows, macOS, and Linux, with smarter dependency management and a switch to the MIT license.
• Major upgrades: Improved statistical methods (IPW, raking, poststratification), interactive Plotly visualizations, and new variance/confidence interval tools.
• Better experience: Enhanced CLI, bug fixes, and expanded docs/tutorials for easier use and learning.

In research and data science, we sometimes encounter biased data: that is, data that has not been sampled completely randomly and suffers from an over- or under-indexing toward the population of interest. Survey data is an example in this regard. Surveys play an important role in providing measurements on subjective user experience indicators, such as sentiment and opinions, which cannot be measured by other means. But because survey data is collected from a self-selected group of participants, it needs to be analyzed carefully.