When Good Graphs Go Bad: Avoid These Misleading Graph Mistakes
Blog post from Sigma
Misleading visuals in data presentations often arise not from intentional manipulation but from rushed decisions, default settings, or unchecked design habits, which can lead to confusion and mistrust. These visuals may break basic promises by showing incorrect data slices, visualizing information in a confusing manner, or implying conclusions not supported by the data, causing viewers to misinterpret the findings and potentially lose trust in the information presented. Issues like vague titles, improper axis scales, wrong chart types, and cluttered design can distort the intended message, while practices like cherry-picking timeframes and over-smoothing data can lead to misleading narratives. To maintain trust in visual analytics, it's crucial to pressure-test charts by ensuring clarity, relevance, and accuracy, and by questioning the visual choices made, to ensure they align with the true story the data is meant to convey.