The Trouble with Daryl Bem

He said: “I’m all for rigor, but I prefer other people do it.”

Daryl Bem is a social psychologist and professor at Cornell University. He has a mixed reputation in the field of research, data, and advanced analytics. Some people see him as a pioneer in the field while others view him as someone who cuts corners and is more concerned with making a point than presenting accurate data.

Bem is quoted as saying, “I’m all for rigor, but I prefer other people do it. I see its importance-it’s fun for some people-but I don’t have the patience for it.” This attitude towards research and data is troubling for many in the field. The article continues: “It’s been hard for him, he said, to move into a field where the data counts for so much. “If you looked at all my past experiments, they were always rhetorical devices. I gathered data to show how my point would be made. I used data as a point of persuasion, and I never really worried about, ‘Will this replicate or will this not?’”

This cavalier attitude towards data is dangerous and puts into question the validity of any of Bem’s previous work. In a field where replicability is key, Bem’s admission that he never worried about whether or not his experiments could be replicated is alarming.

The Importance of Rigor in Research

Rigor is important in research because it ensures that experiments can be repeated and that results are accurate. When research is not rigorous, it calls into question the validity of the entire study. Daryl Bem’s admission that he doesn’t have the patience for rigor is troubling because it means that his past experiments may not be accurate.

Bem is not alone in his attitude towards rigor. There are many researchers who see rigor as unnecessary work that gets in the way of their experiments. However, without rigor, research lacks credibility and cannot be taken seriously.

The Trouble with Data-Driven Experiments

Daryl Bem’s experiments are often criticized for being more focused on making a point than on presenting accurate data. For example, in one experiment, he showed participants images of common objects and asked them to identify them as quickly as…