Making a Difference in How People Use Data
A colleague of mine at Minitab, Cheryl Pammer, was recently featured in "A Statistician's Journey," a monthly feature that appears in the print and online versions of the American Statistical Association's AMSTAT News magazine.
Each month, the magazine asks ASA members to talk about the paths they took to get to where they are today. Cheryl is a "user experience designer" at Minitab. In other words, she's one of the people who help determine how our statistical software does what it does, and tries to make it as helpful, useful, and beneficial as possible. Cheryl is always looking for ways to make our software better so that the people who use it can get more out of their data.
It's exciting that one of Minitab's statisticians was selected to be profiled, and it's always great when someone whom you know does great work receives some public recognition. But I was particularly interested to see what Cheryl had to say about her work at Minitab -- you know, what it is that motivates her to come into work every day. Here's how she answered that question:
A tremendous amount of data exists out there, most of it being analyzed without the help of a degreed or accredited statistician. As a designer of statistical software, my main goal is to promote good statistical practices by presenting appropriate choices to the software user and displaying results in a meaningful way. It is exciting to know that the work I do makes a difference in how thousands of people will use and interpret the data they have.
This really struck home with me. Before I joined Minitab, I worked in higher education as an editor, and I oversaw a magazine that covered the work of scientists from a wide variety of fields. I needed to keep track of circulation and many other metrics, and I did not have a clue how to do it properly. I muddled through it using spreadsheets, and even pencil and paper, but I never had confidence in my conclusions and always had a nagging suspicion that I'd probably missed something critical...something that would either invalidate any good news I'd seemed to find, or would make data that already didn't look so good even worse.
I Needed an Assistant for Data Analysis When I Had No Idea How to Do It
Since then, I've come a long way in terms of analyzing data, even completing a graduate degree in applied statistics. But I remember vividly how it felt to look at a collection of numbers and not have the vaguest idea how to start making sense of it. And I remember seeing research results and analyses and wishing they'd been expressed in some way that was easier to understand if, like me back then, you didn't have a good background in statistics.
And that's where the Assistant comes in. People like Cheryl designed the Assistant in Minitab Statistical Software to help people like me understand data analysis. When you select the type of analysis you want to do in the Assistant -- like graphical analysis, hypothesis testing, or regression -- the Assistant guides you through it by asking you questions.
For example, if you're doing a hypothesis test, the Assistant will ask you whether you have continuous or categorical data. Don't know the difference? Click on a button and the Assistant will give you a crystal-clear explanation so you can make the right choice. Back when I was trying to figure out how our science magazine was performing, this would have saved me a lot of wasted time. It also would have made me a lot more sure about the conclusions I reached.
Describing it doesn't really do it justice, though. Here's a video that provides a quick overview of how the Assistant works:
An Assistant for Data Analysis Is Great Even When You Know How
Even though I've learned a lot about analyzing data since my magazine days, I still find the Assistant tremendously helpful because:
A. I'm usually sharing the results of an analysis with people who don't know statistics, and
B. The Assistant explains those results in very clear language that anyone can understand.
For ANOVA, capability analysis, measurement systems analysis, and control charts, the Assistant's output includes not only graphs and bottom-line results, but also report cards and summaries that tell you how well your data meet the the statistical assumptions for the analysis, and whether there are trouble spots or specific data points you should take a look at. So if you're explaining the results to your boss, your colleagues, or a group of potential clients, you can present the information and provide assurance that the analysis has followed good statistical practice.
We've heard the same thing from consultants, Six Sigma black belts, researchers, and other people who know how to wrangle a data set: these experts certainly can do their analysis without the Assistant, but the Assistant makes it easier to communicate what the analysis means and how reliable it is, both of which are critical.
Which brings us back to Cheryl -- and her colleagues in Minitab's software development teams -- who work so hard to make data analysis accessible to more people. H. G. Wells famously said "Statistical thinking will one day be as necessary a qualification for efficient citizenship as the ability to read and write." In a world where so much data is so readily available to all of us, it's an honor to be part of a team working to make statistical thinking and the ability to make better use of that data more available.