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If there are errors or inconsistencies in your data, you can’t help but end up with misleading graphs. Don’t skip the hard graft of checking for accuracy, cleaning, harmonizing, and streamlining your data before you start, and resist the urge to cherry-pick from conflicting data sources until you get what you want. You should have a single version of truth to work with.2. Choose the Right Type of Data Visualization
Should you use a bar chart? Line graph? Pie chart? Heatmap? Or something else entirely? Each of these types of visualization comes with its own set of benefits and limitations, so think carefully about whether it’s the right way to present this particular set of information.3. Remember that Graphs are Only Part of the Story
All data needs interpretation, even when you have a lovely shiny visualization to help you make sense of it. Make sure you provide as much context, explanation and annotation as possible to make sure that the results aren’t misinterpreted – and never use a graph to push a narrative that you know the data doesn’t fully support! Think you can tell a data disaster from a master misleader? A dodgy design from a deliberate dupe? Take the Grim Graph Quiz, below!