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It takes two to tango: Statistical modeling and machine learning

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  • V. Kumar
  • Mani Vannan

Abstract

Statistical methods (SM) have been dominant in generating insights from any type of data for generations. However, with the recent advances in technology, machine learning (ML) has become one of the widely spoken methods to generate insights with more ease of use. While the followers of statistical methods have a differing view point about ML, and the followers of ML have a differing viewpoint about SM, this article isolates the merits of each of these two methods and advances arguments for when to use what method based on the purpose, context, frequency of use, cost, expertise and time. To be specific, the main purpose of SM is for inference and that of ML is prediction. Further, this article goes one step further and creates a scenario where it shows that when we combine the learning from using a statistical method and apply it to machine learning, the ultimate benefit can be greater than the sum of each method’s benefits.

Suggested Citation

  • V. Kumar & Mani Vannan, 2021. "It takes two to tango: Statistical modeling and machine learning," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 31(3), pages 296-317, July.
    • Handle: RePEc:taf:jgsmks:v:31:y:2021:i:3:p:296-317
    DOI: 10.1080/21639159.2020.1808838
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    1. Li, Yiling & Kim, Hye-jin & Do, Boram & Choi, Jeonghye, 2022. "The effect of emotion in thumbnails and titles of video clips on pre-roll advertising effectiveness," Journal of Business Research, Elsevier, vol. 151(C), pages 232-243.

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