Statistics and the Bad Reputation
In a recent reliability seminar I learned that the younger engineers did not have to take a statistics course, nor was it part of other courses, in their undergraduate engineering education. They didn’t dislike their stats class as so many before them have, they just didn’t have the pleasure.
Generally I ask how many ‘enjoyed’ their stats class. That generally gets a chuckle and opens an introduction to the statistics that we need to use for reliability engineering. I’ll have to change my line as more engineers just do not have any background with statistics.
I suspect this is good new for Las Vegas and other gambling based economies.
Statistics are hard
On average there are a few folks that get statistics. No me. There are those that intuitively understand probability and statistics, and demonstrate a mastery of the theory and application. No me.
I like many others that successfully use statistical tools, think carefully, consider the options, check assumptions, recheck the approach, ask for help and still check and recheck the work. Statistics is a tool and allows us to make better decisions. With practice you can get better at selecting the right tool and master the application of a range of tools.
Sure, it’s not easy, yet as many have found, mastering the use of statistics allows they to move forward faster.
Statistics are abused
Politicians, marketers, and others have a message to support and citing an interesting statistic helps. It doesn’t matter that the information is out of context nor clear. When someone claims 89% of those polled like brand x, what does that mean? Did they ask a random sample? Did they stop asking when they got the result they wanted? What was the poll section process and specific questions? What was the context?
The number may have been a simple count of positive responses vs all those questioned. The math results in a statistic, a percentage. It implies the sample represented the entire population. It may or may not, that is not clear.
We hear and read this type of statistic all too often. We discount even the well crafted and supported statistic. We associate distrust with statistics in general given the widespread poor or misleading use.
To me that means, we just need to be sure we are clear, honest and complete with our use of statistics. State the relevant information so others can fully understand. Statistics isn’t just the resulting percentage, it’s the context, too.
Statistics can be wrong
Even working to apply a statistical tool appropriately, there is a finite chance that the laws of random selection will provide a faulty result. If we test 10 items, there is a chance that our conclusion will show a 50% failure rate even though the actually population failure rate is less then 1%. Not likely to happen, yet it could.
We often do not have the luxury of the law of large numbers with our observations.
So, given the reality that we need to make a decision and that using a sample has risk, does that justify not using the sample’s results? No. The alternative of using no data doesn’t seem appealing to me, nor should it to you.
So, what can we do, we:
- Do the best we can with the data we have.
- Do exercise due care to minimize and quantify measurement error.
- Strive to select samples randomly.
- Apply the best analysis available, and,
- Extract as much information from the experiment and analysis as possible.
As with wood working there are many ways to cut a board, with statistics there are many tools. Learn the ones that help you characterize and understand the data you have before you. Master the tools one at a time and use them safely and with confidence.