Imagine if the true performance of your chips, instead of being something you can validate through simulation or measure in a lab, could only be measured in the

Imagine if the true performance of your chips, instead of being something you can validate through simulation or measure in a lab, could only be measured in the brains of tens or hundreds of thousands of people. For every chip you make, you have some expectation of how it will perform, which is its "value proposition." You want to see if your chips are performing as you predict (fulfilling the value proposition) so you need to interact with a large number of the people whose brains are measuring the true performance of your chips. Since you're dealing with thousands of measurements, you summarize them using math. That's where "stats" come into play. A "metric" is any quantity that is supposed to reflect a measurement, but it usually refers to one specifically designed to be useful. For example, for your chips, the 99th percentile power draw experienced by your users might be a metric you use to guide your design process. Imagine further that your chips perform differently for different groups of people. Figuring out the best way to divide people into groups to understand how your chips perform is "market segmentation." You probably care about some groups more than others, and depending on how well your chips perform for different groups, you may need to strategically change those priorities over time. That's "targeting." Meanwhile, different versions of your chips are continually becoming available to people, and you want to detect as soon as possible if a new chip has an unexpected performance regression (or an unexpected improvement!) So you make the latest metrics available in a way that's quickly visually digestible, usually at the expense of all subtlety, but hey, your brain's executive function finds it gratifying. That's a "dashboard."