Imagine starting a new job and having to drive to the office on your first day. You’ve never been there before, but you know it’s next to a gym you used to go to. From experience, you remember that it typically takes about 30 minutes to get to the gym at that time of day — even though the last time you went was over a year ago (you eventually stopped going, but that’s another story).
Now it’s your first day on the job, and you need to decide what time to leave the house. Let’s imagine you don’t have access to GPS or navigation data. What would you do?
- Use the information you already have (even if it’s outdated) and plan for a 30-minute drive?
- Or ignore everything you know and randomly pick a departure time?
Naturally, the first option makes more sense — you’d rely on the information you have rather than ignore it completely.
Well, if that reasoning makes sense to you, then you’re already on your way to understanding a Bayesian approach to statistics.
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