Box and Whisker Plot

Box and whisker plots (boxplots) provide a standardized way of displaying data distribution based on five key statistics: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. The 'box' shows the interquartile range (IQR), while 'whiskers' extend to show data range, and individual points mark outliers. This visualization is essential for comparing distributions across groups, identifying skewness, and detecting outliers in your data.

Example Visualization

Box and whisker plot comparing gene expression across 4 genotypes with significance brackets

Try this prompt

"Create a publication-ready box plot comparing 'Gene Expression Levels' (normalized counts) across 4 genotypes: WT (Wild Type), KO1 (Knockout 1), KO2 (Knockout 2), and Mutant. Generate a realistic dataset with n=20 biological replicates per group, with KO1 showing upregulation (~1.5x WT), KO2 showing downregulation (~0.8x WT), and Mutant showing moderate increase (~1.2x WT). Overlay jittered individual data points with transparency. Perform pairwise t-tests against WT control and add significance brackets with stars (* p<0.05, ** p<0.01, *** p<0.001, ns for non-significant). Use a colorblind-friendly palette, add y-axis label with units, and include sample size (n=) in x-axis labels."

Generate this now

Python Code Example

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Console Output

Output

Generated dataset: 80 samples across 4 genotypes
          count      mean       std       min       25%       50%       75%       max
Genotype                                                                             
KO1        20.0  1.433506  0.242010  1.010082  1.229742  1.435316  1.549965  1.963070
KO2        20.0  0.794662  0.164170  0.447392  0.697058  0.805572  0.928675  1.011424
Mutant     20.0  1.190581  0.333626  0.414076  1.043802  1.207683  1.444461  1.669393
WT         20.0  0.965740  0.192006  0.617344  0.870256  0.953171  1.101635  1.315843

Pairwise t-test p-values against WT:
KO1: t=-6.772, p=6.439e-08
KO2: t=3.029, p=4.454e-03
Mutant: t=-2.612, p=1.386e-02

Significant genotypes (p<0.05): ['KO1', 'KO2', 'Mutant']

Common Use Cases

1Comparing experimental groups in scientific research
2Detecting outliers in datasets
3Analyzing test score distributions
4Quality control in manufacturing

Pro Tips

Overlay individual data points for small datasets

Use notched boxplots to compare medians visually

Add statistical significance annotations when comparing groups