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.