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.
