This means the numbers are meaningless and don't provide a good representation. Needs better data.
Call me inhumane, but a single story doesn't mean anything. It's just some random point in the set. Drawing any conclusions from such a single point is dangerous (the larger the set, the more), as we humans just love to extrapolate single points and even tend have quite strong emotional defenses about their importance.
To remove the emotional part, just think of something from IT, like response times or test coverages. See, a story of an obscenely long API response (out of thousands) doesn't make much sense anymore. Debugging individual cases may even lead you on a completely wrong track. Unless you want to merely resolve that particular single request.
I'm sorry about the tone. Stories about others make humans relate (which is good), but they also have such undesirable effects (hype over facts, extrapolating, etc).
I'll call you inhumane then. Despite record employment levels and rising average net worth in the USA, millions of people are still starving and struggling with drug addictions and so on.
Averages and generalizations only tell a portion of the story. Anecdotes can shed light on "noise".
I'm not talking about "drawing conclusions from a single point of data". I'm talking about using single points of data to interrogate the completeness and correctness of your data.
They aren't blind spots, they're outliers. If a change to medicare makes 99% of users significantly better off and 1% significantly worse, then it's a net good change. The useful reporting tells both sides, but simply telling the sobstory of one person really negatively impacted is worthless.
Completely agree. Additionally, data segmentation is important. For example, if you segment employment data by college degree, you would see that overall employment has not risen at all for those without a college degree.
It's like saying the average net worth of Jeff Bezos and 99 homeless people is 1 billion USD, etc etc