We live in a time when data is king. Data is everywhere – and it’s tempting to believe that, the more data we have, the more accurate our decisions will be. But regardless of the data, even the most simple graphs can easily mislead you. Even when using the best tools available, your conclusions may not be as sound as they seem if you can’t spot some fundamental issues.
In this series, we’ll be taking bite-sized looks at some of the most common data analysis mistakes made when building and interpreting graphs.
With graphs – as with all your business decisions – context is critical.
Your perception of a situation is easily skewed if there isn’t a representative baseline. This is usually because your axes don’t start at 0, or you’re missing a comparative value.
Take the graph below for example. It shows the 2016 Olympics 100m sprint final times. You might look at this and initially think that Usain Bolt was simply dominant in the field. Did you notice the axis scale though?
That’s right, we’re only comparing the sprinters over 0.25 seconds! The competition looks much closer when you look at exactly the same dataset but with an appropriate baseline:
Imagine if you were shown the two bar charts above comparing different project budget options – would you have made the right choice?
Even still, would you know if a 9.8 second 100m sprint is a good result? Let’s anchor the value with the sprint times of the average non-athlete:
Now we can really see just how close the competition is amongst these elite athletes!
Unfortunately, some news networks are renowned for this exact scaling problem – and if you’re not keeping an eye out for it, it’s very easy to be misled!
When viewing a graph, ask yourself questions like:
- what number do the axes start from, and why?
- is there another value that could be included for context?
This will ensure you actually take the time to understand the scale of the graph and avoid any spurious scaling issues.
If you’re using graphs to make data-driven decisions, it’s important that they are accurate and reliable. Graphs can be misleading if the graph type, colours or scales are poorly chosen, so look out for these details before making a decision.
There is no such thing as a “perfect” graph – every graph has limitations! However, there are many ways in which we can improve our understanding of any given dataset by carefully considering all aspects of the analytical process.
Vaxa Analytics offers free analytics audits to help elevate your business intelligence with insights from experienced professionals who understand what makes a good analysis tick.
Contact us today for more information on how we can work together to create data-driven, actionable insights to help your business succeed.