The numbers never lie, or do they?

I have a friend who is a successful business consultant. One of his many sayings is, "the numbers never lie". In context, he is referring to the profit and loss statements and balance sheets. But, when it comes to statistics, our conclusions are not always accurate; the numbers do not always represent the truth. This is because we become victims of Confirmation Bias.

Confirmation bias is a blind spot that causes us to look for data or evidence to support our presuppositions. In other words, we approach our research or data evaluation with a pre-existing idea or belief. Then we look for the data that will support our belief. Concurrently, we also reject data that opposes our belief. So, when we compile our research into a report, the conclusions are skewed. The result: the conclusions are biased at best.

Though I am certain that some people will do research like this intentionally for personal or political gain, other do it unconsciously. Confirmation bias is a blindspot that affects our ability to report the facts as they are discovered, even if they prove our presupposition to be incorrect.

In a recent Opinion article published in the Wall Street Journal entitled: Trump, Social Science and Media Bias, the writer describes how "a dubious study finds a correlation between Russian troll activity and [Trump's] 2016 polls."

When it comes to statistical data, one would do well to remember the adage, "correlation does not equal causation."

The media’s desire to delegitimize the 2016 election has created an extremely high demand for questionable work that can confirm reporters’ prejudices under the guise of science.

The best way to mitigate confirmation bias is to first recognize that it exists. We need to own the idea that we might have a presupposition that we unconsciously want to support with our research. In order to bring some perspective, carefully contemplate questions like these as you approach your research. The bigger the potential consequences of your conclusions, the more you should be contemplative.

  1. Am I hoping for a specific outcome from this research?
  2. Am I worried or concerned that the research will prove me wrong?
  3. Am I willing to be wrong about my belief or idea?
  4. How will I be affected if my findings prove my assumptions were wrong?
  5. Is my job in jeopardy?
  6. Will my pride be wounded?
  7. Is there anything else that would cause me to be biased in my conclusion?

After you carefully identify and accept the potential for your own biases, then approach the research by asking:

  1. Am I interpreting the data in context? Or, am I possibly trying to extrapolate data out of context in order to justify my position?
  2. Is the source of my data reputable?
  3. Is there enough data to reasonably support the conclusion?

Finally, get other perspectives on your conclusions. Ask others to give you their perspective about your conclusions. Be cautious to not just ask people that you suspect share your views or will support your findings. Take risks and ask people to play devil's advocate for you.

When making larger decisions with greater the consequences, consider engaging others in the entire decision process.

No one is immune from blindspots. We all have them. Blindspots affect us at different times and to different degrees. We just need to be more conscious of them and employ practical tools to mitigate their affects on our behaviors and decision-making processes.

What are your leadership blind spots? Free assessment reveals all. Find out at

Kevin McCarthy, author of the bestselling book: BlindSpots – Why Good People Make Bad Choices, and holds the highest certification recognized globally by the speaking industry, the Certified Speaking Professional. Kevin and his team expose the invisible barriers that impact culture, operations, training, service and leadership.

Sample the Blind Spots Inventory Assessment to see how you can identify the blind spots that are impacting revenues.


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