Misinformation (and the fighting thereof)

One of the banes of our corporate existence is the existence of myths. (We seem to be immune to conspiracy theories, at least.) I’ve been fighting them in myriad ways, over the years. Approaches include a book, talks, and more. We also need ways to vet new information for veracity. Here are a few steps taken recently for misinformation and the fighting thereof.

First, at the Learning Development Accelerator (LDA), we created a research checklist (warning: members only, but at the free level). This was supposed to be a way to vet claims, starting with the practical, but eventually getting into actually evaluating the research. We don’t necessarily recommend this, by the way. It’s probably better to trust research translators unless you’re really willing to dive into the details. (Translators: folks who’ve demonstrated a reliable ability to both take research and extract the meaningful principles and cut through hype).

Then, Matt Richter, my colleague in the LDA, recommended Alex Edman’s book May Contain Lies. I’ve read it and found it an accessible and thoughtful treatment of analyzing claims and data (recommended). Matt even prompted the LDA to host a ‘meet the author’ with Alex. That’s available to view (may also require free membership).

In it, he reiterated something in the book that I found valuable. He talked about a ‘ladder’ of investigation. Telegraphically, it’s this:

1. Statement is not fact (the statement must be accurate)
2. Fact is not data (the fact must be representative)
3. Data is not evidence (the data must be conclusive)
4. Evidence is not proof (the evidence must be universal)

What is being said here is that there are several steps to evaluate what folks want to tell (sell) you. If someone just quotes a statement, it’s not necessarily valid unless it’s accurate. Someone could make a claim that’s not actually true (as happens). Then, that statement alone is not data, unless the statement is representative of the general tenor of thought. For instance, a few positive anecdotes aren’t necessarily indicative of everyone’s experience. Then, representative quotes actually have to be sufficient against any other explanations for the same outcome. For instance, finding out that people like something may not be indicative of its actual efficacy. Finally, the evidence has to apply in your situation, not just theirs.

He used some examples, for instance books where they draw inferences from a few successful companies, without determining that other companies with the inferred characteristics also succeed. What’s nice is he has boiled down what can be an overwhelming set of rules into a simple framework. Misinformation isn’t diminishing, it even seems to be increasing. There’s increasing needs to separate out bogus claims for legitimate. We need to be rallying around misinformation and the fighting thereof. Here’re some tools. Good luck!