Supercommunicators and Hotspots

25 Mar 2024

Charles Duhigg faced a dilemma. By all public accounts, he was considered a great communicator: a New York Times columnist, a bestselling book author, and a Harvard MBA. Yet, he found his daily interactions with his family and at work to be trying and at times unsuccessful. Confounded, he surveyed the field of research for the best communication practices today. His latest book, Supercommunicators catalogs his findings.

Recommended: Mugging

8 Aug 2023

One of my favorite—and incredibly goofy—examples of how we instinctively “model” our environment comes from Amazon.

The Pauling Effect

3 Aug 2023

A friend of mine who is very bright makes his living as a data scientist that specializes in fantasy sports. Recently, he said, “I’m steeped in data and modeling. And I looked at the studies and some of the models on SARS-CoV-2 and hydroxychloroquine [HCQ] and found some of them persuasive,” to the point that he wanted to acquire HCQ and ignore advice to wear a mask.

The Problems of Product “Differentiation”

24 Jun 2021

It seems obvious that there are advantages to standing out from the crowd—to differentiate your product or service from the others in your space. But successful differentiation doesn’t mean that people will choose your product. In fact, asking and acting on the issue of differentiation has at least two important problems when seen through the lens of Jobs to be Done (JTBD).

Anomalies: Improving Good Theory

29 Aug 2020

There is another important consequence of the power of anomaly: all good theories must be susceptible to anomalies, otherwise there is no progress. Correlation is like a sliding scale of confidence–it’s akin to an analog signal that we receive with varying degrees of fidelity. Consequently, we can seldom state definitively if some event is an anomaly or merely “noise.” On the other hand, a causal model is more like a digital signal—it gives us discrete “yes” or “no” indicators for anomalies. As we mention in the first chapter, the causal approach to manufacturing created clear go/no-go signals that either kept work moving or initiated an investigation into a defect. Without the clear binary signals, sub-par work would inevitably sneak through.

How an Anomaly Focus Eliminates Bias

The current widespread focus on the problems of replication in scientific studies—especially those of the social and biomedical sciences—has been to invest in ever more sophisticated analytical tools and techniques: more data, more sophisticated tools of analysis, faster computing, etc. All of these efforts have missed an alternative solution: causality. Most research in biomedical and social science begins with data analysis. But data analysis alone is not capable of yielding a genuine causal model. Essentially, all analysis is subject to the problem of induction—roughly, correlation is not causation.

False Negatives

26 Mar 2020

A few days ago I wrote a post on how easy it is to have “false negatives”—negative for virus, but the person is sick. I made a table to show how easy it is to overestimate the efficacy of a test.