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Don’t Shun the ‘S’ Word

1 August 2011 2,476 views 2 Comments
Nancy Geller


As children, we learned never to use the “S” word. But the “S” word I am talking about this month is not the one our parents and teachers did not want to hear. My “S” word is “Statistics,” or maybe “Statistician.”

All too frequently these days, the “S” word is not used and should be. One example is the February 11, 2011, issue of Science, which was a special edition on data. Little reference was made to the role of statistical science in this special issue. In it, we see many articles about data—ensuring a data-rich future in the social sciences, challenges and opportunities in mining neuroscience data, the future of genomic data, signal processing and the data deluge, and visualizing data—yet the “S” word is mentioned infrequently. The introduction states, “… [T]wo themes appear repeatedly: most scientific disciplines are finding the data deluge to be extremely challenging, and tremendous opportunities can be realized if we can better organize and access the data.” It is clear that Statistics and Statisticians play a huge role in making these data interpretable, yet the “S” word is notably missing.

At least one Statistician wrote a letter to the editor of Science complaining about this omission. Unfortunately, no letter was published. Several representatives from the ASA and AAAS Section U (Statistics) met with Alan Leshner, director of AAAS, who said candidly that Statistics had little visibility in science and we had much work to do to increase our visibility.

A completely different example is a new discipline called “business analytics.” Business analytics software such as that provided by SAS, for example, promises better business decisions based on “data about customers, suppliers, operations, performance, and more.” Business analytics promises to deliver “data analysis tools” and “predictive insights.” Clearly, these insights are coming out of statistical analysis, but nowhere do we see the “S” word.

Now, it is true that the “S” word does not trip lightly on the tongue and is not particularly catchy. But, it is an identity we must strive to maintain. Perhaps we have shied away from the “S” word, ourselves, because nonstatisticians often find us intimidating. But, if the end result is that we are becoming invisible and not getting credit for our achievements in lending insight to the data explosion, we do ourselves and our discipline a great disservice. If the “S” word falls into disfavor and disuse, I fear our discipline will lose its identity and, instead of a single discipline, Statistics will become subservient to data analysis, data mining, bioinformatics, business analytics, etc.

How can we stop being invisible? We need to wear our identity as a discipline proudly, in both professional and social settings. On your job, there are plenty of opportunities to brag a little about the achievements of statistical colleagues. When a new project is discussed, be sure to point out the potential contributions Statistics could make to the project results.

In social settings, when asked what you do, be sure to use the “S” word. Even with someone whose reaction is the inappropriate “You are??” don’t retreat! Explain some of the achievements of Statisticians, whether they are in medicine, social science, business, engineering, or training the next generation of Statisticians. We need to tell people that Statisticians are the ones who make sense of the data deluge occurring in science, engineering, and medicine; that Statistics provides methods for data analysis in all fields, from art history to zoology; that it is exciting to be a Statistician in the 21st century because of the many challenges brought about by the data explosion in all of these fields. Do not forget to note that Statistics is a discipline worth pursuing as a career because the need for gleaning insight from the data now being collected is only going to grow. Think up a favorite example (or two) so you will be prepared the next time someone asks you what you do for a living.

I am proud to be a Statistician, and I hope you are, too. Express your pride whenever you have the opportunity. The future of Statistics is in your hands!

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  • Jerzy said:

    When giving examples of what statisticians do, I always enjoy sharing Gosset’s story: the consistent quality of beer today is partly thanks to his t-test, essentially invented at Guinness. On top of that, it’s pretty nifty that they considered their statistician to be a “secret weapon,” making him publish under a pseudonym to keep their industrial advantage a secret from other brewers.
    But of course there are plenty of more recent good examples!

  • Harlan said:

    Statisticians are by no means the only professional group dealing with this issue! I recently attended the business-focused conference of INFORMS, the professional society for Operations Research and Management Science. The membership of that society is aggressively trying to attach itself to new buzzwords like Analytics, defining the term “Advanced Analytics” to cover various sorts of non-trivial statistical analysis, statistics-based forecasting, and the types of large-scale numerical optimization that that field is justifiably famous for.

    I haven’t talked to anyone in Machine Learning about this recently, but I’m sure that ML people are annoyed that the buzzword is not just “Machine Learning”.

    And then of course there are new terms like Data Science and Data Journalism, which are umbrella terms for (in the first case) any sort of application of scientific statistical/ML/OR methods, or (in the second case) application of statistical/ML/OR/data visualization methods to mostly-public data for journalistic ends rather than peer-reviewed publication or profit.

    It will be interesting to see how this proliferation of terms shakes out. I, for one, hope that the relevant professional societies, including ASA, can join forces to better define the terms, the goals, and the philosophies of applied data analysis, and the critical role of formal academic and professional training in the various data-analysis-related fields.

    Harlan Harris, Washington DC