This reminds me of something I’ve heard—that a data visualization is badly designed if different people end up with different interpretations of what the data visualization is saying. Similarly, we want to minimise the possible misinterpretations of what we write or say.
Each time I add another layer of detail to the description, I am narrowing the range of things-I-might-possibly-mean, taking huge swaths of options off the table.
Nice point, I’ve never really thought about it this way, yet it sounds so obvious in hindsight!
Choosing to include specific details (e.g. I like to eat red apples) constrains the possible interpretations along the key dimensions (e.g. color/type), but leaves room for different interpretations along presumably less important dimensions (e.g. size, variety).
I have a tendency to be very wordy partly because I try to be precise about what I say (i.e. try to make the space enclosed by the moat as small as possible). Others are much more efficient at communicating. I’m thinking it’s because they are much better at identifying which features are more relevant, and are happy to leave things vague if they’re less critical.
Just some thoughts I had while reading:
This reminds me of something I’ve heard—that a data visualization is badly designed if different people end up with different interpretations of what the data visualization is saying. Similarly, we want to minimise the possible misinterpretations of what we write or say.
Nice point, I’ve never really thought about it this way, yet it sounds so obvious in hindsight!
Choosing to include specific details (e.g. I like to eat red apples) constrains the possible interpretations along the key dimensions (e.g. color/type), but leaves room for different interpretations along presumably less important dimensions (e.g. size, variety).
I have a tendency to be very wordy partly because I try to be precise about what I say (i.e. try to make the space enclosed by the moat as small as possible). Others are much more efficient at communicating. I’m thinking it’s because they are much better at identifying which features are more relevant, and are happy to leave things vague if they’re less critical.