When reading a report and there is a large complex formula, maybe a derivation, do you just skip over it? Does a phrase such as 95% confidence of 98% reliability over 2 years not help your understanding of the result? When you read about hypothesis testing, confidence intervals, point estimates, parameters, independent identically distributed variables, random samples, orthogonal arrays, do you just shiver a bit? Do you know of folks around you who not only do not understand these terms but do not want to understand them?
SIGNS OF AVERSION TO STATISTICS
“That result isn’t right.” I actually had this conversation, where after I presented results on an experiment to improve factory yield, my boss said that he didn’t believe the conclusion, as it didn’t fit with what he expected. He basically waved his hand over the analysis of measurement error, the data-collection procedure, the analysis, and conclusions and said, “Yeah, that’s not right.”
He couldn’t explain it other than by saying that he wanted a different result.
“What does this mean?” is an approach that may have two meanings. You did something novel or advanced and your audience truly wants to know more and understand. Said a different way, it may imply that they just want to get the overview and just the results — they are not going to sort through the math and analysis to obtain an understanding.
“I’m off for a weekend in Vegas” says your colleague. Although such a statement is not a sure sign that he does not understand statistics, for it is possible he does enjoy the many entertainment opportunities found in gambling casinos, it may indicate that he haven’t grasped the nature of the world around them. The law of large numbers requires a lot of numbers before it applies. Just because he has lost money on the last six trips does not mean that he is due for a big win; that would actually be a flawed bit of logic.
We hear that statistics is tough or that is wasn’t covered in school and there are questions about why one sample isn’t enough. We face people who are uninterested, unwilling, or maybe unable to grasp that variance is a measure of dispersion and that shifting the mean about all day long will not help reduce the variability of the results.
IT’S NOT YOU
Sure, many folks, including you, would prefer to use a simple, easy way to calculate value and move on. The mean time between failures is just such a measure: It’s just an average, and most of us get that. Beyond it being just an average, we separate those who ‘get’ statistics from those who don’t. You are risking confrontation by suggesting that we need to work on reducing the standard deviation, not because it is the right thing to do (how dare you come up with a good idea!), but because you are bordering on topics that your peers do not fully understand.
Learning and mastering statistical tools takes time and practice for many, but most lack the time and interest. You most likely have taken the time and have found some success with various statistical tools. So, what can you do to continue to make progress improving your product or system?
- Continue to use statistical tools.
- Document and layout the approach, the analysis, and the results clearly.
- Find at least one colleague who appreciates what you’re doing.
- Let the results and benefits speak for themselves. (Include benefits, results, and value created in your reports and presentations — lower failure rates may go unnoticed otherwise.)
- Keep looking for ways to apply statistical tools. Get better results and continue to make a difference.
Bio:
Fred Schenkelberg is an experienced reliability engineering and management consultant with his firm FMS Reliability. His passion is working with teams to create cost-effective reliability programs that solve problems, create durable and reliable products, increase customer satisfaction, and reduce warranty costs. If you enjoyed this articles consider subscribing to the ongoing series at Accendo Reliability.