Asking The Right Questions To Get The Right Data
Insights from 'Thinking, Fast and Slow' by Daniel Kahneman
Thinking fast and slow is a book largely about what is wrong with our thinking.
But its intention isn't to leave us feeling like we failed a test but rather to get us thinking about our thinking and specifically how we could better process data to inform our choices.
Related Content: Watch More Emeritus Insights Videos From Best-Selling Books
Today, data is abundant and often overwhelming.
The secret to getting the best insights from data is to ask the right questions.
This was something that the employees at the bill and Melinda gates foundation didn't do before they made a $1.7 billion dollar investment into schools in the U.S.
You see the research they had received included statistics that explored the characteristics of the most successful schools in the US.
What it is that makes schools successful is a topic that fascinates researchers, governments, and philanthropists-- all wanting to know what distinguishes the most successful ones from the rest.
One of the clear findings of this research was that on average, the top-performing schools were small schools.
Now, this data encouraged the Gates Foundation and several other prominent institutions, including the u.s government, to make a sizable investment into the creation of small schools, including providing larger schools, with funding to break up into smaller units.
Even without statistics, this would make intuitive sense, right?
Smaller schools allow for more focus on the quality of teaching and attention to individual students especially those that struggle with learning.
Unfortunately, whilst these statistics might have been correct, the facts drawn from them were not.
The statisticians who reported to the Gates Foundation had only asked what the characteristics of the best-performing schools were as that is all they were interested in.
However, if they had asked the same question about the worst-performing schools, they would have had a totally different picture.
You see, the worst-performing schools were also small schools!
It was clear as day in the statistics, but no one asked about them.
If anything, large schools tended to produce better results on average.
Now, this is an example of a confusing correlation with causality.
Because the limited stats made intuitive sense no one sought to look at the data any further.
This would have helped them get a fuller picture.
Now, what about you?
Where are you only seeing half the picture?
What better questions could you ask of the data that informs your choices?
About Emeritus Insights
Emeritus Insights is a daily learning platform with 5000+ lessons from the world’s best-selling books, top faculty, subject matter experts and exclusive lessons from Harvard Business Review. Get insights in bite-sized video lessons, share it with friends and colleagues and learn across a wide range of topics to shape your success.