Eston Martz
Published: Wednesday, February 8, 2017 - 12:02 The language of statistics is a funny thing, but there usually isn’t much to laugh at in the consequences that can follow when misunderstandings occur between statisticians and nonstatisticians. We see these consequences frequently in the media, when new studies—that usually contradict previous ones—are breathlessly related, as if their findings were incontrovertible facts.
Similar, though less visible, misinterpretations abound in meeting rooms throughout the business world. When people who work with data and know statistics share their analyses with colleagues who aren’t well-versed in the world of data, the message that gets received may be very different than the one the analyst tried to send. There are two equally vital solutions to this problem. One is encouraging and instilling greater statistical literacy in the population. Obviously that’s a big challenge that can’t be solved by any one statistician or analyst. But we individuals can control the second solution, which is to pay more attention to how we present the results of our analyses, and enhance our sensitivity to the statistical knowledge possessed by our audiences. I’ve written about the challenges of statistical communication before, but I’ve been thinking about it anew after a friend sent me a link to this post and subsequent discussion about replacing the term “statistical significance.” I won’t speculate on the likelihood of that proposal, but it feels like a good time to review some words or phrases that mean one thing in statistical vernacular, but may signify something very different in a popular context. Here’s what I came up with, presented in a tabular form: Say the word... Statisticians mean... Most people mean... Assumptions Constraints within which we can do a particular analysis, such as data needing to follow a normal distribution Bias, prejudices, opinions, or foregone conclusions about the topic or question under discussion Confidence A measurement of the uncertainty in a statistical analysis The strength with which a person believes or places faith in his or her abilities or ideas Confounded Variables with effects that cannot be distinguished Confused, perplexed, or inconvenient Critical value The cutoff point for a hypothesis test A measurement, sum, or number with great practical importance—such as a minimum cash balance in a checking account Dependent A variable that’s beyond our control—such as the outcome of an experiment An outcome or thing we can control or influence (e.g., “Going to the party is dependent on completing my work.”) Independent A factor we can control or manipulate An outcome or thing we cannot control or influence (e.g., “They will make the decision independent of whatever we might recommend.”) Interaction When the level of one factor depends on the level of another Communications and social engagements with others Mean The sum of all the values in your data divided by the number of values (sX/n) An adjective signifying hostility or, in slang, positivity (e.g., “That mean response surprised us all.”) Mode The most frequent value in a data set A manner or method of performing a task (e.g., “You’ll finish faster if you change your operating mode.”) Median A data set’s middle value Intermediate or average; so-so Normal Data that follow a bell-shaped curve Something that is commonplace, ordinary, plain, or unexceptional Power The capability to detect a significant effect Degree of control or influence Random A sample captured such that all individuals in a population have equal odds of selection Unpredictable; beyond control Range The difference between the lowest and highest values in a data set An array or collection Regression Predicting one variable based on the values of other variables Retreat or loss; moving backwards Residuals The differences between observed and fitted values Leftovers; scraps Significance The odds that the results observed are not just a chance result Importance or seriousness Can you add to my list? What statistical terms have complicated your efforts to communicate results? For Eston Martz, analyzing data is an extremely powerful tool that helps us understand the world—which is why statistics is central to quality improvement methods such as lean and Six Sigma. While working as a writer, Martz began to appreciate the beauty in a robust, thorough analysis and wanted to learn more. To the astonishment of his friends, he started a master’s degree in applied statistics. Since joining Minitab, Martz has learned that a lot of people feel the same way about statistics as he used to. That’s why he writes for Minitab’s blog: “I’ve overcome the fear of statistics and acquired a real passion for it,” says Martz. “And if I can learn to understand and apply statistics, so can you.”
Management Common Words With Precise Statistical Meanings
Clarifying the familiar but foreign language of statistics
About The Author
Eston Martz
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Comments
*light bulb*
This was quite an eye opener, and further intensified my dislike of math word problems. It’s also a reminder you have to adapt to your audience. If you are going to talk to other statistically minded peers they yes speak the language. But you will have to remember those of us that are not possessing degrees in the field are going to give you the deer in the headlights look. I like that this article points out were not stupid or inferior it places emphasis on miscommunication and the importance of identifying possible misinterpretations. As I went down this list I could understand its use in terms of statistics but found myself more comfortably in the far right definition out of habit. A few of these made some lectures and discussions suddenly snap into focus. I agree you can lead a horse to water but you can’t make them research definitions. I also agree trying to educate everyone would be quite a task. At this time you have the power to educate as you communicate the information, but also providing readily available statistic to English translation is a step in the “Accurate” direction.
Words Mean Things!
Precise: Statisticians mean repeatable with little variation. Most people mean "accurate".
Accurate: Statisticians mean on target. Most people mean "the right answer".