Statistics are persuasive.
So much so that people, organizations, and whole countries
base some of their most important decisions on organized data.
But there’s a problem with that.
Any set of statistics might have something lurking inside it,
something that can turn the results completely upside down.
For example, imagine you need to choose between two hospitals
for an elderly relative’s surgery.
Out of each hospital’s last 1000 patient’s,
900 survived at Hospital A,
while only 800 survived at Hospital B.
So it looks like Hospital A is the better choice.
But before you make your decision,
remember that not all patients arrive at the hospital
with the same level of health.
And if we divide each hospital’s last 1000 patients
into those who arrived in good health and those who arrived in poor health,
the picture starts to look very different.
Hospital A had only 100 patients who arrived in poor health,
of which 30 survived.
But Hospital B had 400, and they were able to save 210.
So Hospital B is the better choice
for patients who arrive at hospital in poor health,
with a survival rate of 52.5%.
And what if your relative’s health is good when she arrives at the hospital?
Strangely enough, Hospital B is still the better choice,
with a survival rate of over 98%.
So how can Hospital A have a better overall survival rate
if Hospital B has better survival rates for patients in each of the two groups?
What we’ve stumbled upon is a case of Simpson’s paradox,
where the same set of data can appear to show opposite trends
depending on how it’s grouped.
This often occurs when aggregated data hides a conditional variable,
sometimes known as a lurking variable,
which is a hidden additional factor that significantly influences results.
Here, the hidden factor is the relative proportion of patients
who arrive in good or poor health.
Simpson’s paradox isn’t just a hypothetical scenario.
It pops up from time to time in the real world,
sometimes in important contexts.
One study in the UK appeared to show
that smokers had a higher survival rate than nonsmokers
over a twenty-year time period.
That is, until dividing the participants by age group
showed that the nonsmokers were significantly older on average,
and thus, more likely to die during the trial period,
precisely because they were living longer in general.
Here, the age groups are the lurking variable,
and are vital to correctly interpret the data.
In another example,
an analysis of Florida’s death penalty cases
seemed to reveal no racial disparity in sentencing
between black and white defendants convicted of murder.
But dividing the cases by the race of the victim told a different story.
In either situation,
black defendants were more likely to be sentenced to death.
The slightly higher overall sentencing rate for white defendants
was due to the fact that cases with white victims
were more likely to elicit a death sentence
than cases where the victim was black,
and most murders occurred between people of the same race.
So how do we avoid falling for the paradox?
Unfortunately, there’s no one-size-fits-all answer.
Data can be grouped and divided in any number of ways,
and overall numbers may sometimes give a more accurate picture
than data divided into misleading or arbitrary categories.
All we can do is carefully study the actual situations the statistics describe
and consider whether lurking variables may be present.
Otherwise, we leave ourselves vulnerable to those who would use data
to manipulate others and promote their own agendas.
