Whether 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:
- Sophistication in statistics compensates for lack of data and/or business understanding.
- Extracting meaning out of randomness
- Correlation versus causation – modeling will help uncover causal relationships
- Extrapolating the models way beyond the permissible limits
- 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