Data visualisation and interpretation issues

why do we visualise data

We visualize data to discover patterns, think deeply about the data, and see differences between groups and individuals. Data visualization is also about communication: it tells a story, helps us understand the data, and presents it clearly to the reader.

What are the key principles of data visualization?

Data visualization should be guided by scientific questions and not be used for “fishing” for results. Consider aesthetics carefully, such as using colours that are visible to all (avoiding problematic colours for colour blindness). Don’t just show summary statistics like the mean—also show the distribution and variance. Look at individual data points to truly understand your data and uncover hidden stories.

common ways to visualise data

histograms, density plots, box plots, violin plots

common issues with data interpretation

Common issues with data interpretation include confusing relative versus absolute risk, sometimes making absolute measures more appropriate, manipulating axis scales (such as truncating an axis), and cherry-picking time periods to misrepresent trends.