P.Mean >> Category >> Human side to statistics (created 2007-06-16).

Although statistics involves numbers and formulas, it also involves human interactions. You provide statistical analysis in the context of a team effort to examine a research question, and this means that you need to be aware of human issues in the production of statistics. Articles are arranged by date with the most recent entries at the top. You can find outside resources at the bottom of this page. Other entries about the human side to statistics can be found in the human side to statistics page at the StATS website.


44. P.Mean: I get a letter published in the Kansas City Star (created 2013-08-26). I try to write regularly to the Letters to the Editor page of the Kansas City Star. Most of the letters I write are about politics and are irrelevant to the topics on this web site. Today, though, I got a letter published about research.

43. P.Mean: Resource for statisticians called as legal experts in a trial (created 2013-08-14). I found a couple of nice PowerPoint slideshows that outline some of the issues that you should consider if you are hired to present an expert opinion about a statistical matter in a court of law.

42. P.Mean: Placing the mind of a statistician into software (created 2013-02-19). Someone asked about whether the American Statistical Association was going to "place the mind of a statistician into software." This means, I presume, creating a computerized system that could think like a statistician thinks. I was a bit skeptical and here is my dour reply.


41. P.Mean: Are you qualified to make that comment? (created 2012-10-08). Dear Professor Mean, What level of knowledge should a statistician have of the field to be able to safely comment on the use of statistics there?

40. P.Mean: Questions for a panel on statistical consulting (created 2012-02-08). I am participating on a panel discussion about statistical consulting. The organizer suggested several questions that we might want to tackle if there are not that many questions from the audience. I thought they were pretty interesting questions.

39. P.Mean: Promoting your consulting career in the era of web 2.0 (created 2012-01-27). I am giving a short course in February, "Promoting Your Consulting Career in the Era of Web 2.0." Here is an outline of what I will talk about.


38. P.Mean: Statistics is more than just cutting computer code (created 2011-09-06). Someon on LinkedIn asked how to react to a comment like "Statistics is just cutting computer code, right?" Here's how I responded.

37. P.Mean: What goes on your business card (created 2011-08-04). Someone who was about to embark on an independent consulting career asked me what you should put on your business card. My advice was to keep it simple.

36. P.Mean: Using Social Media to promote your consulting career (created 2011-08-01). I am leading a roundtable discussion of using Social Media to promote your consulting career. Here are some things that I plan to discuss.

35. Second invitation to talk about how independent consulting is different (created 2011-07-24). I have a second invitation to talk on how independent consulting is different in 2012 (send me an email if you're curious about when and where). I was asked to submit an abstract, so here it is.

34. Promoting your consulting career in the era of Web 2.0 (created 2011-05-20). I was approached by a member of the planning committee for the American Statistical Association Conference on Statistical Practice about giving a talk at that conference. The talk would be an extension of a roundtable discussion I am giving at the Joint Statistical Meetings in 2011, Using Email Newsletters, Webinars, Blogs, And Social Media To Promote Your Consulting Career. After a telephone call this morning, I offered to prepare an abstract of a talk that I might give at this conference. I'm very flexible on the content of this talk, but I thought it would be a good idea to put my thoughts down in writing.

33. Is it ethical to provide statistical consulting on a disseration to a Ph.D. candidate (created 2011-05-11). Someone asked a hypothetical question about consulting assistance for a Ph.D. candidate. Clearly some assistance is okay and the question is when the work becomes so much that the work is no longer perceived as that of the Ph.D. candidate.

32. How independent consulting is different (created 2011-05-09). There's a huge difference between independent consulting and any of these other forms of consulting. I want to identify some of the major differences that I have experienced as an independent consultant.


31. P.Mean: Would hire someone who knew theory or someone who knew practice (created 2010-11-03). Someone on LinkedIn asked if it was better to hire someone who knew theory or someone who knew practice. Here's my response.

30. P.Mean: What should clients get from you at the end of the first consulting session (created 2010-08-14). There has been a lot of discussion about the nature and role of consulting on the message boards of the Statistical Consulting section of the American Statistical Association  One particularly valuable question was what should you do when starting a new consulting job. Here is an adaptation of one particularly good response.

29. P.Mean: How do I handle criticism (created 2010-05-21). Someone asked how I handle criticism. To be honest, I don't get criticized all that much. Possibly it is that I do very little that deserves criticism, and possibly, people are intimidated by the area I work in (unjustifiably intimidated, by the way, but many people are just plain scared of numbers). It is also important to note that most people don't like to share negative opinions directly. They certainly will tell others, of course, if something is wrong, but it takes some boldness and some bravery to confront a person directly.

28. P.Mean: Lessons learned the hard way: don't throw good money after bad (created 2010-05-14). I am helping out with data management for a project involving 19 million records from an insurance database. The file is too big to be read into R in one piece, so I decided to read in successive segments of 100,000 records and then write them out again as separate files. This was a big mistake and showed me the importance of the saying: "Don't throw good money after bad."

27. The Monthly Mean: The deadly sins of researchers: envy (April 2010)

26. The Monthly Mean: Relying on experts in an area where you have no particular expertise (February/March 2010)

25. P.Mean: What to say when any data analysis is pointless (created 2010-03-25). Someone on the MEDSTATS email discussion group asked for help. They were trying to establish a normal range or reference interval for a set of observations involving gastric emptying. The sample size, 14, was much too small to produce reliable results, but it got worse than that. For one of the outcomes, the result was fourteen zeros. What can you do with such a data set? What can you say? That a difficult question, and here is how I would approach such a problem.

24. P.Mean: Consulting remotely versus consulting in person (created 2010-02-08). Someone was asking whether there is a trend in consulting to demand a local presence rather than allowing a consultant to work remotely. I was unable to comment on work trends, as I have only been an independent consultant for 14 months. I did point out, however, some of the issues associated with remote consulting.

23. P.Mean: What are the characteristics of a good statistical consultant (created 2010-02-07). Someone was considering a career as a statistical consultant. Besides building up a network and gaining experience, what traits would be necessary to be successful in such a career?

22. The Monthly Mean: The deadly sins of researchers: wrath (January 2010)

21. P.Mean: Masters or Phd in Statistics? (created 2010-01-19). Someone asked me about careers in Statistics and if you get the best career with a Masters degree or a PhD. That's a very subjective choice and individual preferences should weigh strongly in your choice.


20. The Monthly Mean: Are we statisticians gods? (December 2009) and P.Mean: Are we statisticians gods? (created 2009-10-13). I'm helping someone who wants an alternative statistical analysis to the one used by the principal investigator. I'm happy to help and will offer advice about why my approach may be better, but I was warned that the PI considers the analysis chosen to be ordained by the "Statistic Gods" at her place of work. I'm not sure what to make of the words "Statistic Gods".

19. The Monthly Mean: The deadly sins of researchers: lust (November 2009)

18. The Monthly Mean: The deadly sins of researchers: gluttony. (September/October 2009)

17. The Monthly Mean: The deadly sins of researchers: sloth (July/August 2009)

16. P.Mean: Analyzing bad data (created 2009-05-22). A discussion on the MEDSTATS email discussion group centered around a data set involving blood loss. Blood loss was quantified into categories with values of less than 250 ml, 250-500 ml, 500-1000 ml, and great than 1000 ml. The discussion centered on the inefficiencies created when continuous data is reported in categories like these.

15. The Monthly Mean: The deadly sins of researchers: pride (May/June 2009)

14. The Monthly Mean: Research scientists are truly are special people (March/April 2009)


13. P.Mean: Statisticians are not gatekeepers (created 2008-11-04). A discussion of the proper role of statisticians when presented with questionable data is raging in the MedStats discussion group. I added some comments recently about the dangerous tendency for us statisticians to view our roles as "gatekeepers". Here's the gist of my comments.

12. P.Mean: Refusing to analyze a data set (created 2008-10-28). An associate of mine has a problem. He has been told by a statistician that they can't analyse his data because it is not from a randomised trial. I personally feel that there is no problem with doing any sort of analysis with this data group.

Outside resources:

Don Zimmerman. Devilish Dictionary for Statisticians. Description: This webpage offers some irreverent definitions of statistical terms, akin to Ambrose Bierce's The Devil's Dictionary. They are all very cynical and very funny. Here's an example: "Sample--a rag-tag, bob-tailed bunch of atypical misfits who have volunteered to participate in an experiment." [Accessed March 25, 2010]. Available at: mypage.direct.ca/z/zimmerma/devilsdictionary.htm.

Susan A. Peters. Engaging with the Art and Science of Statistics. Mathematics Teacher. 2010;103(7):496. Abstract: "Statistics uses scientific tools but also requires the art of flexible and creative reasoning." [Accessed September 24, 2010]. Available at: http://www.nctm.org/eresources/view_media.asp?article_id=9145.

R A Parker. Estimating the value of an internal biostatistical consulting service. Stat Med. 2000;19(16):2131-2145. Abstract: "Biostatistical consulting is a service business. Although a consulting biostatistician's goal is long-term collaborative relationships with investigators, this is the same as the long-term goal of any business: having a group of contented, satisfied customers. In this era of constrained resources, we must be able to demonstrate that the benefit a biostatistical consulting group provides to its organization exceeds its actual cost to the institution. In this paper, I provide both a theoretical framework for assessing the value of a biostatistical service and provide an ad hoc method to value the contribution of a biostatistical service to a grant. Using the methods described, our biostatistics group returns more than $6 for each dollar spent on institutional support in 1998." [Accessed August 14, 2010]. Available at: http://www.ncbi.nlm.nih.gov/pubmed/10931516.

Stephen Jay Gould. The Median Isn't the Message. Prefatory Note by Steve Dunn: "Stephen Jay Gould was an influential evolutionary biologist who taught at Harvard University. He was the author of at least ten popular books on evolution, and science, including, among others, The Flamingo's Smile, The Mismeasure of Man, Wonderful Life, and Full House. As far as I'm concerned, Gould's The Median Isn't the Message is the wisest, most humane thing ever written about cancer and statistics. It is the antidote both to those who say that, "the statistics don't matter," and to those who have the unfortunate habit of pronouncing death sentences on patients who face a difficult prognosis. Anyone who researches the medical literature will confront the statistics for their disease. Anyone who reads this will be armed with reason and with hope. The Median Isn't the Message is reproduced here by permission of the author." [Accessed November 19, 2009]. Available at: http://cancerguide.org/median_not_msg.html.

Journal article: Shari Messinger. No shortcuts when collaborating Amstat News. August 1, 2011. Excerpt: "As a collaborating statistician, I am often asked by researchers to 'run their data' so they can get the answers they seek corresponding to a particular investigation. What they are really requesting (usually) is that I perform data analysis to address their research questions—some of them quite vague—and that I first need to determine an appropriate analytic approach based on the nature of the investigation, study design, distributional properties of the data, and particular research objectives. Although I know this is really what they are requesting, I am not sure if they are really aware of what is involved." [Accessed on August 17, 2011]. http://magazine.amstat.org/blog/2011/08/01/statscienceaug11/.

Michael Lavine. Introduction to Statistical Thought. Excerpt: "Introduction to Statistical Thought grew out of my teaching graduate and undergraduate statistics courses for many years, and from my experience as a statistical consultant and collaborator. I wanted to write a text that * explains how statisticians think about data, * introduces modern statistical computing, and * has lots of real examples. The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly. By "statistical thinking" is meant a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. By "likelihood emphasis" is meant that the likelihood function and likelihood principle are unifying ideas throughout the text." [Accessed October 20, 2010]. Available at: http://www.math.umass.edu/~lavine/Book/book.html.

Interesting quote: The statistician who supposes that his main contribution to the planning of an experiment will involve statistical theory, finds repeatedly that he makes his most valuable contribution simply by persuading the investigator to explain why he wishes to do the experiment, by persuading him to justify the experimental treatments, and to explain why it is that the experiment, when completed, will assist him in his research.  -- Gertrude M. Cox. (I can't recall the original source where I found this quote. Sorry!)

Nick Bilton. Using Facebook Updates to Chronicle Breakups - NYTimes.com. The New York Times. 2010. Excerpt: "Working with Lee Byron, an information interaction designer at Facebook, Mr. McCandless recently chronicled another interesting data set: when couples break up. The two designers answered this question by scanning over 10,000 Facebook status updates over a yearlong period and then plotting them on an annual graph." [Accessed November 3, 2010]. Available at: http://bits.blogs.nytimes.com/2010/11/03/using-facebook-updates-to-chronicle-breakups.

Creative Commons License All of the material above this paragraph is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2017-06-15. The material below this paragraph links to my old website, StATS. Although I wrote all of the material listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright ownership of this material. The brief excerpts shown here are included under the fair use provisions of U.S. Copyright laws.


11. Stats: When a client asks for a bad analysis (March 24, 2008). I received an email from someone who was being asked to perform a subgroup analysis that is likely to produce confusing and counter-intuitive results. I was asked to help draft some language to convince the client that this was a bad idea.


10. Stats: Grow up and learn how to do Statistics (November 8, 2007). I attended a talk by Dr. Martha Curley about parental presence during invasive procedures and resuscitation. Early in the talk, Dr. Curley mentioned a bell shaped curve and mentioned "the statistician in me" which was a surprising but appreciated revelation (Dr. Curley is a faculty member of the University of Pennsylvania School of Nursing).

9. Stats: What does a statistician do all day? (September 17, 2007). I have to write up a job description for a statistician. This includes essential functions and important functions. Here are two examples.

8. Stats: The danger of providing expert testimony when you are not an expert (January 31, 2007). Sir Roy Meadow is an expert on child abuse, having published a landmark paper in 1977 on a condition known as Munchausen Syndrome by Proxy. An observation of his, "one sudden infant death in a family is a tragedy, two is suspicious and three is murder, unless proven otherwise" became knows as "Meadow's Law". In testimony at the trial of a woman, Sally Clark, who had two children who died from SIDS, Sir Meadow tried to quantify this statement by arguing that the chances of observing two SIDS deaths would be 73 million to one.


7. Stats: Should (Can?) Statistical Consultants, be Independent? (December 14, 2006). I attended a webinar, "Should (Can?) Statistical Consultants, be Independent?" presented by Janet Wittes. Statisticians are like Humpty-Dumpty (When I use a word...), in that there are certain words (validated, prespecified, intent-to-treat, and independent) have special meanings just to statisticians. The talk focused on the last word, independent. Independent is not ignorant or uninterested (although disinterested meaning lack of conflict of interest is good).

6. Stats: Rebutting an expert reviewer (November 6, 2006). A regular contributor to EDSTAT-L (KW) asked about how to handle a bad peer review of an article that a colleague had submitted. The reviewer appeared to get the definitions of positive and negative skewness backwards.

5. Stats: Always ask why (May 8, 2006). I have a three year old boy at home and he's learned that one way to keep the conversation going with an adult is to simply ask the question, "Why?" I'll say "We're going to church this morning" and he'll say "Why?" I'll say, "Because it's Sunday" and he'll say "Why?" At this point, I'm stumped. Why exactly is it Sunday today and not Tuesday. Or in the morning I'll point out that it's raining outside and he'll say "Why?" And I have to struggle with an answer like, "When there is too much moisture in the air, it falls down to the ground in the form of precipitation." At work when people ask me to do something, I need to emulate my little boy and and ask them why. Not in a hostile way, but to get them to talk some more so I can find out exactly what they want.

4. Stats: Surviving Statistical Spitting Matches (April 25, 2006). I generally dislike an outline or bullet format for presenting information, but I came across a website that provides such valuable information that I am willing to overlook the lack of narrative text. The title of this web page is quite provocative "Surviving Statistical Spitting Matches" and there is a lot of good advice.

3. Stats: When is a co-authorship warranted? (April 4, 2006). I am co-author on over 60 papers and have helped with the publication of many more papers. What does it take to get a co-authorship? I don't quibble a lot about this, but it seems everyone has their own standard. When I am asked, I tend to discourage listing me as a co-author if all I did was perform a routine (routine to me, anyway) data analysis. If the analysis is very difficult and/or uses new and uncommon approaches, then I would tend to seek co-authorship. Also, if I helped with writing a substantial section of the paper itself, co-authorship is probably warranted.

2. Stats: The role of a statistician on an IRB (March 29, 2006). Someone on the IRBForum wrote in and asked what guidance their IRB could offer to a statistician who was just assigned to an Institutional Review Board (IRB). There were many good responses. I wrote in with a few comments of my own. A statistician, especially one fresh from graduate school, might feel a bit perplexed at all the issues that arise in an IRB. Certainly they are not qualified to answer questions like whether lumbar puncture qualifies as minimal risk. But they can and should offer unique contributions to the IRB.


1. Stats: Media interview tips (March 23, 2005). I have not had many requests for interviews, but I work a lot with people who talk to the media all the time. It's not an easy job, but it is a very important job. Scott Berry writes about his experiences with discussing models that predict outcomes in sports with radio talk show hosts and print media reporters.

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Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2017-06-15.