Sometimes, I wonder just how wealthy I'd be if I had a dime for every time I heard the term "quality" or "quality improvement" in a meeting followed by absolutely no data. Unless someone is asking a question such as "How can we characterize our current performance?" or "How should we look into our current state?" it does not get us far to discuss our current quality or system improvements without data...and we do just that routinely in healthcare. Without data, we often oversimplify performance as "good" or "bad" and fail to appreciate both incremental improvement and potential solutions.
There are lots of reasons why this happens in healthcare especially: inability to collect data (eg no staff to collect data because data are seen as unimportant), lack of expertise to interpret data, cultural resistance to letting our data guide us...I could go on. A classic sophism that does this better than I can is "Without data, you just have an opinion." Just how important is Statistics for quality improvement in healthcare and other fields?
Stop by the Minitab blog for a great review on why Statistics is so important for quality improvement, and more on the reasons why Mark Twain's criticism of of the field as "lies, damn lies, and statistics" is catchy, but just plain wrong.
Remember, without good data (a good sample represented properly) and a good analysis we aren't able to reach good conclusions--and perhaps the only thing worse than changing a system based on poor conclusions is sharing an opinion with no data at all! Perhaps Deming said it best: "In God we trust. All others must bring data."
For just a bit on how Statistical knowledge is vital for quality improvement, look here:
If you want to use data to learn how the world works, you must have this statistical knowledge in order to trust your data and your results. There’s just no way around it. Even if you are not performing the study, understanding statistical principles can help you assess the quality of other studies and the validity of their conclusions. Statistical knowledge can even help reduce your vulnerability to manipulative conclusions from projects that have an agenda. The world today produces more data than ever before. This includes all branches of science, quality improvement, manufacturing, service industry, government, public health, and public policy among many other settings. There will be many analyses of these data. Some analyses are straight up for science and others are more partisan in nature. Are you ready? Will you know which conclusions to trust and which studies to doubt?