Category: Human side to statistics. 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. This category has not yet been assigned to a theme. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.
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.
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).
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.
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.
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).
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.
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.
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.
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.
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.
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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This webpage was written by Steve Simon on 2007-06-16, edited by Steve Simon, and was last modified on 2008-07-08. This page needs minor revisions.