Statistical Analysis: Common Mistakes

caution2Whether you are a beginner in the field or an expert in statistics, the article by Dubey and Rajaram, 5 Common Mistakes People Make in the Name of Statistical Analysis, is a must read. The paper starts with this excellent example:

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.

The five following mistakes are detailed and explained by the authors:

  1. Sophistication in statistics compensates for lack of data and/or business understanding.
  2. Extracting meaning out of randomness
  3. Correlation versus causation – modeling will help uncover causal relationships
  4. Extrapolating the models way beyond the permissible limits
  5. Imputing missing values with mean or median is the best way of treating missing values

This article is an excellent reminder for practitioners and I strongly advise it.

Read the full article: 5 Common Mistakes People Make in the Name of Statistical Analysis


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