Scientific Chart Type Selector
Answer three questions about your data and research goal. Get a specific chart recommendation with a direct link to create it in Plotivy.
Why Chart Choice Matters for Scientific Papers
Choosing the wrong chart type is one of the most common mistakes in scientific manuscripts. A bar chart when you should use a dot plot. A pie chart when a stacked bar communicates the same information more precisely. A 3D surface when a 2D heatmap is clearer.
The right chart depends on three things: your data type (continuous, categorical, temporal), how many variables you are displaying, and what story you need the figure to tell. A scatter plot reveals correlations. A box plot reveals distributions. A line chart reveals trends. Mixing these up obscures your results instead of communicating them.
Reviewers notice chart choice. A well-chosen visualization builds credibility and makes your argument self-evident. A poorly chosen one raises questions about the researcher's understanding of their own data.
Frequently Asked Questions
When should I use a bar chart vs a dot plot?
Use bar charts for counts or when comparing magnitudes where the baseline (zero) is meaningful. Use dot plots (Cleveland dot plots) when you have many categories, when bars would be visually overwhelming, or when individual data points are more informative than bars. For small-n biological experiments, journals increasingly prefer dot plots that show every data point.
Is a pie chart ever appropriate for scientific data?
Rarely. Pie charts work only when you have 2-4 categories and the proportions are distinctly different. For anything more complex, a stacked bar chart or treemap communicates proportions more accurately because humans compare lengths more reliably than angles.
How do I visualize data with both categorical and continuous variables?
Group your continuous data by the categorical variable. Box plots, violin plots, and grouped bar charts all handle this pattern. For two continuous variables colored by a categorical variable, use scatter plots with color or shape encoding per group.
What chart should I use for comparing multiple treatment groups?
For 2-5 groups with continuous outcomes: grouped box plots or violin plots with overlaid individual data points. For many groups: a dot plot or forest plot. Always show statistical test results (significance brackets or p-values) when comparing groups in a scientific context.