ethics and number crunching

On Friday, Andrew Gelman at Columbia (his blog: was on a panel about ethics at the University of Wisconsin-Madison. He specifically talked about ethics of publishing statistics research and open data. He addressed a few topics that you can read about in his first Chance column here [Link]. Responses to this article are here [Link]. He has another article about statisticians not practicing what they teach [Link].

As a statistician, I think the key point is to recognize that different analyses can give different perspectives on a data set. I am not suggesting that researchers be regularly subjected to forensic analyses of all their decisions in data collection and analysis, explaining every email exchange or every new version of a data set that had a transformation or data exclusion. But openness should be the norm.

Gelman mentioned that too often extreme case studies are used in classroom studies with a clear Evildoer. These case studies are not good for helping students deal with ethics of what he called “tough cases” where there isn’t an obvious conclusion. The tough cases have nuances and shades of gray, so they can trivialize ethical issues, implying that anything is ethical if you align it a certain way (i.e., if you are a weasel).

Another interesting point was about doing bad statistics. Being incompetent isn’t unethical, but if someone tells you that you should redo your analysis because your conclusions cannot be supported and you don’t, then your refusal to do better science can be unethical. Case in point, these ridiculous US Department of Transportation “forecasts.” Clearly bad science. The US DOT continued to use the forecasts years after their data suggested that the forecasts were way off.

The US Department of Transportation has been making the virtually identical vehicle travel forecasts for well over a decade. These forecasts project rapid and incessant growth in vehicle travel while actual traffic volumes have decreased.

Gelman pointed out that operations research often addresses different ethical concerns regarding how to allocate scarce resources when there is not enough to go around. I’ll write more about that another time.

Please share ethics issues in the comments.

2 responses to “ethics and number crunching

  • Heider Jeffer

    Reblogged this on Heider Jeffer.

  • Ivan Taylor

    The most common ethical issue that I have experienced in operational research is the request for a study that supports a decision that already has been made and the rejection of a study that does not support that decision.

    Operational researchers can be asked for advice but organizational leaders make the final decision. if you want to continue working in or for an organization, you better not disagree with the boss or client too often. Thus your choice might only be to just not publish or do work that is contrary to the common view of the situation.

    Another problem I have found is that operational researchers need data. If members of the organization do not trust you to support their position, then they will withhold their cooperation and not release data to you. Thus the problem is not restricted to leaders in an organization but to anyone who considers the operational researcher as a threat.

    The bottom line is that it is easy for people to accept and utilize work by operational researchers that supports their already held views and it is easy to discard or sabotage operational research studies that disagree with their views.

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