There are some statistical concepts that all data visualization practitioners should know about, and the concept of statistical significance is one of them.

It's a hard concept to grasp because it requires one to think beyond the data that are collected. The abstract thinking is necessary since we typically want to make general statements - while using the collected data as evidence.

My new video in the Data Science: The Missing Pieces series explains statistical significance. To be precise, it explains NOT statistically significant. When something is not significant, it causes all sorts of anxieties, panics, half-measures, re-examinations, and havoc. Much of the time, the result is confusion and misinterpretation.

The video addresses a recent news item - Instagram's experiment to hide the Like count. See for example this article. After running this experiment, Instagram's analysts will look for statistical significance. If the result is NOT significant, what does it mean?

Check out the video for more.

 

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DSTMP3_thumb_significance

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