Just 100 years ago, very few statistical tools were available and the field was largely unknown. Since then, there has been an explosion of tools available, as well as ever-increasing awareness and use of statistics.
While most readers of the Minitab Blog are looking to pick up new tools or improve their use of commonly-applied ones, I thought it would be worth stepping back and talking about one that was used before we had so many options at our disposal: the i-Test.
Like most early analysis tools, the i-test had to be simple to compute because computers were not available. It simply returned a binary response—"pass" or "fail" in most cases—that was usually meant to confirm or deny some prior assumption. This equates to what we call the null and alternative hypotheses today.
But even without a calculator or computer, in most cases this test was lightning-fast to use and virtually any employee had it at their disposal, unlike current statistical tools.
No one has ever been credited with its invention, and as far as I can tell from my research, it has never been documented in any peer-reviewed journals. Perhaps it was obvious enough that it was simultaneously developed by multiple scholars around the same time.
Given that better tools and computing power were not available at the time, those using it had quite the advantage over those who did not, so it quickly became a tool that virtually everyone used. Just as with the modern statistical tests you are familiar with doing in Minitab, the combination of simplicity, accuracy (at least for the time), and power made it a necessary tool for anyone wanting to make decisions based on someone thing other than a guess or intuition.
Then came along the tools and computers we have today, and something sad and unfortunate happened. As people learned these new techniques, which were clearly more powerful and even more accurate, the i-test was almost completely forgotten as a potentially useful method among those who could suddenly do ANOVA and regression and t-tests.
It seemed the only people still relying on it were those who didn't understand modern statistics, and they were soon being easily outperformed. The few who cling to relying solely on the i and reject what statistics have to offer are usually mocked.
But I've noticed something. While those using modern statistical tools to make decisions have been easily outperforming those who still rely on the outdated i-test, there is a subset of people who use both. They rely on p-values and R-Squared and confidence intervals, but they also employ the old standby i-test, just to make sure it agrees with what the statistics are telling them.
These people are the highest performers, devastatingly accurate in their analysis and rarely making a bad decision based on data. They are confident in their results because they've double-checked them and easily convince others of what they've learned. They are James Bond of the data analysis world, embracing the latest technology while never letting go of timeless values!
You probably call it the eye-test. Make sure it never leaves your ever-growing toolbox.