There are very few things I hate, very few. Currently, getting hot flashes is up there. But of wider relevance these days is my abhorrence of two (somewhat interrelated) things: generalizations and simplification.

When you simplify, you lose meaningfulness. Without even bringing up issues in the media which are worth discussing, e.g., misleading headlines, parody taken as truth, redirecting attention by making mountains out of molehills, agenda-driven “news”, or the predilection to take memes at face value (we can save all of that for another day), let’s just look at how people want information.

People lap up ratings, ranks, scores. What are the top party schools, best colleges, affordable towns? What about the best cities for young singles, money-saving apps, countries to be educated in, states for retirement? And what about the highest crime rates, gun crimes, homelessness? Or even something like S&P ratings, Energy Star scores and GPAs?

Many factors go into each number. When you take a result at face value and don’t try to understand the criteria used, size of the pool surveyed, wording of the questions or the extent of the factors that went into each single final judgement, you risk missing the bigger picture.

The way this was first driven home to me was from the first TED Talk I ever saw, The best stats you’ve ever seen by the late Hans Rosling. The Swedish professor’s 2006 talk looks at child mortality, income, hunger across different countries over time and breaks the numbers down and then introduces his software which transforms data from different sources into visual magic, something way ahead of its time. My boss was impressed with how the presentation software operated, I was forever changed by hearing why range and not averages matter and is meaningful.

So, what does any of this have to do with flooding, racism, or how Israel is rated less peaceful than Syria and Iran? It’s not enough to say be a skeptic about what you read (though you may also want to learn more about cognitive dissonance and confirmation bias, where people often reject truths to fit their narratives). More importantly, when presented with ratings, rankings and scores, take a moment to research how the conclusions were reached. Filter for those factors that matter to you and those that do not.

Because when you take a number or a simplified explanation (or a sound bite or a meme…) as gospel truth, you run the same risk as when you label groups. While the irony of saying how all generalizations are awful doesn’t escape me, it’s really true. And it’s really what I’ve been writing about since I started this blog three weeks ago.

The way to get past simplifications with ratings and scores is to not to accept them but to seek out the detailed data points that go into them.

And the way to past generalizations about people is, simply put, to (1) recognize our own reasons for bias against groups and that institutional bias on behalf of groups both exist; (2) get past bias by understanding how labels divide and by befriending individuals; (3) see capitalism doesn’t have to mean denying the satisfaction that comes from belonging to (and caring about) something bigger than ourselves.

Please, let’s stop simplifying and generalizing, and instead enrich the repository of individual truths and details we carry with us wherever we go.