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Quotable | 9 Ways to Spot Bogus Data

We’re hard at work editing chapters for our Year 1 data literacy book. While we’re rolling around ideas, here are some ideas from Geoffrey James’ “9 Ways to Spot Bogus Data” in Inc., subtitled “Decision-making is hard enough without basing it on data that’s just plain bad.”

If you don’t know what some of these questions are asking, stay tuned … we’ve got you covered. Soon, anyway.

Good decisions should be “data-driven,” but that’s impossible when that data isn’t valid. I’ve worked around market research and survey data for most of my career and, based on my experience, I’ve come up with touchstones to tell whether a set of business data is worth using as input to decision-making.

To cull out bogus (and therefore) useless data from valid (and therefore potentially useful) data, ask the following nine questions. If the answer to any question is “yes” then the data is bogus:

  1. Will the source of the data make money on it?
  2. Is the raw data unavailable?
  3. Does it warp normal definitions?
  4. Were respondents not selected at random?
  5. Did the survey use leading questions?
  6. Do the results calculate an average?
  7. Were respondents self-selected?
  8. Does it assume causality?
  9. Does it lack independent confirmation?

Let us know which of these you’d like to see unpacked in a future blog post!

Kristin

 

One thought on “Quotable | 9 Ways to Spot Bogus Data

  1. #1: Should this include non-profits that may gain political power or increased donations?
    #6: Say more on “Do the results calculate an average?”
    #8: Assuming or concluding causality is often unjustified, but how does it make the data bogus?

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