We are constantly surrounded by information in today’s digital world, but not all of
it is accurate.
We rely on data to gauge whether something is true or false, but we rarely see this data
in its raw form.
You can imagine how rows and rows of numbers could be very confusing to interpret.
Because of this, we usually use a method called data visualisation to present patterns and
trends in data more easily.
Data visualisation is the translation of data into visual representations like charts and
graphs to communicate the data’s significance.
However, while this method simplifies the process of understanding data, it can also
be used to bend the truth and misrepresent information.
One such technique is the process of cherry picking data.
This name comes from the idea that if a cherry picker only picks the healthiest and ripest
fruits, it could lead an observer to wrongly assume that all of the cherries on the tree
are healthy, even though that isn’t the case.
This has been observed in the veterinary industry, where vets are more likely to report only
the positive trials when testing the responses of dogs, cats, horses, cattle and sheep to
novel drugs, especially when the studies have been funded by pharmaceutical companies.
These practices aren’t just limited to pets and livestock: industry trials of drugs for
humans, such as antidepressants, were reported to be much more likely to show positive results
than government-funded studies of the same drugs.
Another way of using data visualisations to manipulate information is the use of cumulative
data instead of annual data.
Cumulative data means adding each successive input in the data set up, so that the graph
will always be rising; annual data, on the other hand, would show the data for each individual
year, which could be increasing or decreasing.
This method is often used by companies to make their sales and figures appear larger
than they actually are.
Data vis manipulations are commonly used in politics to make a particular party or candidate
look more favourable.
For example, pie charts are commonly used to represent numeric proportion, like showing
which candidates people are most likely to vote for.
But consider if participants in the survey were allowed to vote for more than one candidate.
Then you could end up with a pie chart where the sectors add up to over 100%.
This is inaccurate because pie charts are meant to show proportions of a whole where
each group is distinct, and can make it appear as though a larger percentage of voters have
chosen that particular candidate.
A better visualisation would be a venn diagram, to show how many votes each candidate received,
and where they overlap.
Lastly, let’s look at bar graphs: bar graphs can misrepresent data through a manipulation
of the scale of the y-axis.
For example, a politician might want to exaggerate how high school graduation rates have increased
during their tenure.
You can see how the difference between 75% and 80% can seem far more significant when
viewed on a Y-axis that begins at 50 versus 0.
So while not all of the information we are presented with in our everyday lives is inaccurate,
there are a lot of ways in which media outlets, corporations and candidates can (and do) misrepresent
data in order to make it swing in their favor.
From politicians misrepresenting data to appeal to voters, to scientists cherry-picking data
to exaggerate their research progress, data misrepresentation can bend the truth in extreme
ways.
However, now that you’ve seen how data can be manipulated, you can be more wary of the
information you receive and thus make well-informed choices, whether it’s choosing between brands,
candidates or beliefs.