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For effective quality improvement work, data is needed to evaluate if changes tested are resulting in improvement. Quality improvement data is often gathered on a set of measures. Outcome, process and balancing measures promote understanding of the impact of changes on the system. Data is also important to understand the variation in a process, and to monitor the process over time.

The resources below are designed to help you:

  1. Understand how to best visualize your data
  2. Appreciate the limitations of data
  3. Provide instruction on data gathering and analysis


  • Correlation: this video demonstrates why correlation does not imply causality (Video – 10:44)