Statistics and Distortion: What is the Whole Truth?

By Kay Whatley

When I see new statistics released, or reports made public, I often read several sources to understand the topic and data presented.

I try to make sense of the information, and what the industry, government, or private agency has presented and why.  I also try to “read between the lines” — not trying to make assumptions, though no one is perfect. By read between the lines, I mean that I try to look at what is presented and what is not included. There are always bits of data not included, either in the original data collection or in the presentation. It would be impossible for the whole truth to be included; still, sometimes exclusions may seem to be designed to skew conclusions.

I’ll try to give some examples later in this editorial; for now, let me explain how I view data gathering and statistics. I have worked with computer databases and forms creation for different companies, so I understand that choices are made from the out-set, and forms designed to target certain information from the humans who complete them.  I’ve also filled out forms, participated in surveys, and — as I mentioned above — read various releases, reports, and statistics-based conclusions online and in print.

Reports or public releases are generally based on the data collected or statistics available.  My belief is that statistics can have flaws, because whether it is a human or a human-programmed computer sifting through the data, the data collected is extracted and put together often with a human end-result or bias in mind. Humans and the machines that they build are likely to have flaws.

Am I saying that all statistics are unsound?  No.  I mean that statistics are bits of data and can be mashed together in different ways. When they are mashed together, a human can make choices about what is included, or discarded, before the results are considered final. When I read an official presentation of this or that, I try to look at what facts support the presentation, but, also look at which details are included and what potentially important points are not included.

For example, look at what happens when someone passes away. When the death certificate arrives, the cause of death might be listed as cancer; one of the choices available for death certificates. The truth is, the death may have been caused by something such as the side effects of radiation treatment; however, that type of detail is not part of the death certificate. Instead, cancer is listed and cancer deaths are tracked but little detail is given, compounding by the general actions that cancer patients are not automatically autopsied. In this case, the data collected is settled into neatly arranged choices, rather than more specific — harder to fit into a data structure — descriptions for cause of death.

Now, add marketing to the mix alongside statistic.  I don’t want to speak to marketing completely; just to have the understanding that when a company, group, or government agency releases data, they are expending resources to get that information out. It reminds me of a college professor who taught, “Everything is rhetoric.” If a human or group of humans puts out information, it is likely to be designed to not make them look bad, but to enhance and justify their position, even promoting their ideals.

Let’s look at a political example. I am, by no means, an expert in politics. I am instead a reader, listener, and voter trying to make sense of the whole thing. A candidate running for office may say that they believe in a particular issue. Their commercials and advertising will repeat their stand on this issue over and over; however, their competitor will try to point to the times when that candidate has voted against legislation on that issue.  Hmm. Since it is not possible to know what’s going on in the candidates head — or behind closed doors — when that issue is important to me I have to read more about how the candidate really voted and what wording in their commercials could be considered vague or misleading. It’s a flawed process, but one a human has to go through to try to find either the truth or some nuggets of it.

Now, think about the last time that you filled out a form. If you were entering a contest and had to fill out the form to enter for a possible prize, you likely shared personal information about your buying habits, vacation habits, and personal shopping choices. If you were at a medical office, you shared information about who you are, how you feel, problems you might be having, medical history, insurance details, family history, and possibly demographic information — sex, race, age, marital status, and so on. If you completed an application form, you likely shared contact information and job history. Each form picks specific information about you that is desired. Not all forms ask all questions. The data collected is designed for the planned use.

It is the same with research. Therein lies the data collection choices, made by humans or agency computers, about the details wanted versus those that are unwanted.  The question is, why are they unwanted?  If the research is about health, does it include or exclude certain demographic information?  If the research is about a social issue, does it include or exclude certain financial information?  If the research is about a public issue that is a political hot-button, does it include or exclude health or race information?  Why? Are some conclusions already targeted?

For example, a pesticide report was issued by the USDA in January 2016. Testing for pesticides does not include testing for all pesticides, and the omissions of certain chemicals can cause the data to be incomplete or misleading. An article regarding this example is posted online at and includes a link to the USDA.

In conclusion, when I read statistics or reports that seem to imply a certain “truth”, I consider the rhetoric and human viewpoints that might have skewed the results. I look for what is included and what seems unmentioned. I encourage my fellow human beings to do the same. To quote Fox Mulder, “The Truth is Out There.”



Ed. Note: The views and opinions expressed in an editorial or article are not necessarily those of the editors and do not necessarily reflect official policies or positions of The Grey Area newspaper. This information is merely submitted for your consideration. If interested in submitting an article or editorial, email for review.

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About Kay Whatley 2309 Articles
Kay Whatley serves as Editor and Reporter with The Grey Area News. Kay is a published author with over 20 years of experience in the publishing industry. Kay Whatley is wife to Frank Whatley, founder of The Grey Area™ newspaper and The Grey Area News online news website.