A recent news story provides a teachable moment on how summary statistics obscure the whole picture of what an underlying process is trying to communicate. ?? The story proclaims: "???????????? ?????? 375 ???????????????????????? ?????? ?????? ???? 2024, ?? 15-???????? ????????: ???????????????????????? ???????? 12.1%????????????????? ???????? 2023, ???????? ???????????????? ???????????????????????? ?????????????? ?????? ??????, ???? 28.6%." ????????'?? ?????? ?????????????? ????????? The figures are averages, or midpoints of a data series -- where's the rest of the data that goes around them? We're being led to draw conclusions on incomplete data. In the video below, I show the whole data series the averages were calculated against (total monthly insolvencies) as a ?????????????? ?????????????????? ??????????. Straight away we can see signals indicating special causes of variation (the red and orange points), some which I use to guide segmenting the chart to "follow the variation" in the data. Note how each period has variation around a mean. ?? What insights do you get from this compared to the news story? How have insolvencies changed since the first year of the pandemic response? What patterns can you see? ?? ???????????????? ?????? ???????? ???? ???????? ???? ???????? ????????????????????????: are you using summary statistics to lead users to draw conclusions, or are you presenting data in-context, over time in a way that separates signals from noise? What insights are you capturing or missing? Which would you prefer? You can see this chart on my app, ?????? ???????????????? ??????, here: ?? https://lnkd.in/gh8aN6qV
You will never have the privilege of using average if your process is unstable. Plotting the data in its time order gives one power to detect patterns of change as they unfold unlike the averages which "freeze" the data and strip them of their context. You have a great input there. Happy to see people like you talking about the process behavior chart.
Astute observers might notice that the stair-stepping that starts on March 22nd "brackets" periods of 11 months, with the exception of the last period which stands at 12 months. I didn't choose these locations to shift the limits, I just followed the variation to see what patterns emerged. What does this suggest with respect to the variation in how insolvencies accrue month-to-month?