Introduction to Data Science Using R Runestone Version 1

The purpose of asking about stories is that people mainly think in stories. From farmers to teachers to managers to CEOs, people know and tell stories about success and failure in their particular domain. Stories are powerful ways of communicating wisdom between different members of the same profession and they are ways of collecting a sense of identity that sets one profession apart from another profession. The only problem is that stories can be wrong.

If you can get a professional to tell the main stories that guide how she conducts her work, you can then consider how to verify those stories. Without questioning the veracity of the person that tells the story, you can imagine ways of measuring the different aspects of how things happen in the story with an eye towards eventually verifying (or sometimes debunking) the stories that guide professional work.

For example, the farmer might say that in the deep spring frost that occurred five years ago, the trees in the hollow were spared frost damage while the trees around the ridge of the hill had more damage. For this reason, on a cold night the farmer places most of the smudgepots (containers that hold a fuel that creates a smoky fire) around the ridge. The farmer strongly believes that this strategy works, but does it? It would be possible to collect time-series temperature data from multiple locations within the orchard on cold and warm nights, and on nights with and without smudgepots. The data could be used to create a model of temperature changes in the different areas of the orchard and this model could support, improve, or debunk the story.