Essay
When the Data Is Right, but the Comparison Is Wrong
Why more information makes judgment more important, not less.
A reflection on data, comparisons and why human judgment matters even more when information becomes abundant.
I was listening to a video recently where someone said blueberries do not have much vitamin C, so they are not really that useful.
For a moment, I was taken aback.
It sounded convincing because it sounded factual.
Then I stopped and thought:
Why am I even judging blueberries on vitamin C?
That is not the main reason people eat blueberries. If I compare them with oranges only on vitamin C, of course blueberries are going to look bad.
The data may be right.
But the filter I am using to look at it may be wrong.
That thought stayed with me.
We see this in many other places too.
Take two salespeople.
One does $10 million in sales. Another does $6 million.
At first glance, the first person clearly looks better.
But what if one inherited a mature territory with established customers, while the other entered a new market and built the business almost from scratch?
Revenue is still a valid number.
But if I am trying to understand who created more value, is revenue alone the right filter?
Or take two schools.
One has students scoring 90. Another has students scoring 75.
Again, the first school looks better.
But what if the students in the first school entered at 88, while the students in the second entered at 60?
Now what am I trying to understand?
The final score?
The improvement?
The quality of teaching?
The students the school started with?
The data itself has not changed.
The way I look at it has.
I think this matters even more now.
With AI, we are going to come across more data, more rankings, more comparisons and more instant answers than ever before.
And a lot of that data may be completely correct.
So the challenge may not always be spotting what is false.
It may be making sure we do not switch off our own judgment just because something comes with numbers behind it.
Not to become cynical about data.
Not to question everything for the sake of questioning it.
Just to keep asking:
Am I using the right filter to understand what this data is actually telling me?