How should you use an indicator? When you look at the weather report, it seems pretty straightforward. Sunny means you do not need an umbrella. High of 36 means you should wear a coat. But what about when there is a 40% chance of rain? And what if you are trying to figure out if you can go for a hike this weekend?
When talking to my friend Chris Miller recently, he was trying to predict the weather before going on an aggressive hike. He mentioned that the noise in the forecast was part of his signal to decide how seriously he should take the report. If the forecast kept changing in the few days leading up to the proposed hike, that meant there was a decent chance that the weather would be unfavorable the day-of.
This got me thinking about the standard weather forecast as an indicator of the underlying data. I had a friend who was studying to be a meteorologist, and so she would go straight to the NOAA source data to predict the weather. For everyone else the weather report is basically a black box and all we have to work with are the indicators.
And when there are no indicators that answer your question? Or if it is simply impossible to interpret the underlying data? That is when it is time to get creative with the information you do have.
When talking to my friend Chris Miller recently, he was trying to predict the weather before going on an aggressive hike. He mentioned that the noise in the forecast was part of his signal to decide how seriously he should take the report. If the forecast kept changing in the few days leading up to the proposed hike, that meant there was a decent chance that the weather would be unfavorable the day-of.
This got me thinking about the standard weather forecast as an indicator of the underlying data. I had a friend who was studying to be a meteorologist, and so she would go straight to the NOAA source data to predict the weather. For everyone else the weather report is basically a black box and all we have to work with are the indicators.
And when there are no indicators that answer your question? Or if it is simply impossible to interpret the underlying data? That is when it is time to get creative with the information you do have.
No comments:
Post a Comment