2021 marketing meta-science year in review: improving marketing research in 2022 – By Aaron Charlton

Okay so we are starting to learn that there could be some issues with the way that we are doing research currently. That’s okay. It’s part of the process. We can take a hard look, make changes, and come out better for it. Specifically, it looks like there may be a lot of noise mining happening in marketing journals. Here is my oversimplified explanation of noise mining: Instead of using inferential statistics to test our ideas, which is good, we’re using these tools to find ideas, which is usually bad. 

My personal journey into meta-science

I first became aware of the extent of the noise mining problem when I started doing a meta-analysis of mediation tests in marketing and found that nearly all of the test CIs were too close to zero to be trusted. I was shocked! I knew there were a lot of problems with confounds and such but I had no idea that so many of the effects could be just random noise. This was right before the pandemic hit in 2020. We didn’t post our preprint until near the end of 2021 though [1]. In our preprint we leave the door open for the possibility that many of the tests are true results that are simply underpowered, but noise mining is the most parsimonious explanation for the phenomenon. 

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