Working at the Edge of Human Knowledge, Part One: What Makes Statistics Cool
What is it like to explore the unknown? Whether it’s academic research or a quality improvement initiative, everyday ordinary people explore the unknown, solve mysteries, and answer questions that have never been answered before. To do this, researchers spend a lot of time reading research papers and develop their knowledge so that they know the full extent of existing knowledge in their research area. Then, the researchers take an amazing step. They find a research question that will take them beyond the edge of human knowledge, a question that no one has answered before. This is the transition from understanding existing knowledge to creating new knowledge.
Going beyond the edge of human knowledge sounds like an exciting place to work. And, it is. Scientists and quality specialists do this every single day. However, it doesn’t mean you’ll always hear about our work on the news. The questions and answers are new, but not always newsworthy. So, most of us work in anonymity. Bit by bit, the bubble of human knowledge is pushed further and further out as new answers are discovered. The results are recorded in the ever-growing number of journal articles, which are, in turn, studied by researchers who then ask more new questions. In this fashion, we push back the frontier of what is known.
One of the coolest things about the field of statistics is that statistical analysis provides you with a toolkit for exploring the unknown. Christopher Columbus needed a lot of tools to navigate to the New World and make his discoveries. Statistics are the equivalent tools for the scientific and quality improvement explorer because they help you navigate the sea of data that you collect. You’ll be increasingly thankful for these tools when you see a worksheet filled with numbers and you’re responsible for telling everyone what it all means. On top of that, there is the excitement of discovery which happens after all of the work to setup the experiment, collect the data, verify it, and arrange it in Minitab. Finally, there is that moment when you click “OK” in one of Minitab’s dialog boxes and the meaning behind the data is revealed to you. That’s why I love it!
Having good data is a prerequisite for using all of the fantastic tools that Minitab statistical software offers. Because researchers work with questions that haven’t been answered before, it’s not surprising that we run into obstacles for collecting good data. Often, these are novel problems. Indeed, some types of data may not have been collected before, which may require studies about how to collect that data accurately and precisely before we can even begin to answer the main research question. On good days, you can happily think of these difficulties as creative challenges. On a few days, it felt like I was just solving one problem after another, after another, etc. After all, we’re trying to generate nice and neat data that we can analyze, but reality is messy.
That interaction between messy reality and neat, usable data is an interesting place. It’s a place that ties together the lofty goals of scientists to the nitty gritty nature of reality. It’s a place that I’ve written about extensively in my blog and I plan to continue to do so. It’s where the rubber meets the road. If you don’t get good data, you can’t answer your research question despite the slick tools in Minitab!
In my next blog post, I’ll detail one of those “rubber meets the road” instances that were part my early on-the-job education!