How to Support your Data Interpretations

Whenever we attribute meaning to the results of a data project, we are interpreting those results. We’re using what we know about the data, the analysis, the project as a whole and all kinds of preexisting knowledge, opinions and worldviews to say, “ah, if that is the result of this analysis, then that means…”.

This isn’t unique to data science, it is the foundation of all science from deciphering the results of a supercollider to humans figuring out that if you strike certain rocks together, you can make sparks. Have question > gather data > process data > interpret results.

Sometimes our interpretations are correct, but sometimes they aren’t. Sometimes we don’t have enough data, or we have flawed data, or our methodology can’t give us the answer we want, and sometimes our biases, preconceived notions, and prejudices keep us from getting to the correct interpretation.

The possibility of a flawed interpretation is causing a lot of problems for data science today. If an interpretation can be flawed, it can’t be automatically trusted. If your data results are ‘open to interpretation’, what good are they? The desire to have your data project taken seriously, be believed, and not be dismissed leads data producers to bury the very notion of interpretation. ‘This is a fact!’ proclaim the scientists. ‘That’s your opinion!’, yells…