Not much difference between communicating to self and communicating to others

July 7, 2012

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Thomas Basbøll writes:

[Advertising executive] Russell Davies wrote a blog post called “The Tyranny of the Big Idea”. His five-point procedure begins:

Start doing stuff. Start executing things which seem right. Do it quickly and do it often. Don’t cling onto anything, good or bad. Don’t repeat much. Take what was good and do it differently.

And ends with: “And something else and something else.”

This inspires several thoughts, which I’ll take advantage of the blog format to present with no attempt to be cohesively organized.

1. My first concern is the extent to which productivity-enhancing advice such as Davies’s (and Basbøll’s) is zero or even negative-sum, just helping people in the rat race. But, upon reflection, I’d rate the recommendations as positive-sum. If people learn to write better and be more productive, that’s not (necessarily) just positional.

2. Blogging fits with the “Do it quickly and do it often” advice.

3. I wonder what Basbøll thinks about the “Don’t repeat much” advice. His blog is interesting but for awhile he seems to have been going with the same big idea (structure your writing as paragraphs, write them one at a time).

4. Davies’s advice to try different things contradicts the usual recommendation to “market the brand of you.” I can try different things because I already have a brand of me (and part of that brand is that I try different things), but that can’t work for everybody.

5. I’m also reminded of the message I often give in the context of statistical graphics, that there is not much difference between communicating to self and communicating to others. This fits with Basbøll’s attitude, I think. Clear writing is not just about marketing an already-formed idea; it’s also about exploring and explaining it to yourself.

Please comment on the article here: Statistical Modeling, Causal Inference, and Social Science

Tags: , , ,