Five Common Mistakes People Make in the Name of Statistical Analysis
Imagine you are a regional sales head for a major retailer in U.S. and you want to know what drives sales in your top performing stores.
Your research team comes back with a revealing insight – the most significant predictor in their model is the average number of cars present in stores’ parking lots.
Really?
The team has fallen victim to one of the most common mistakes people make in the name of statistical analysis: confusing Correlation with Causation. Today’s “Information Management” newsletter includes an article we wrote on the topic.
To learn the remaining four mistakes, check out the article, and let us know what you think!