As first step, we will load the same dataset used before and visualize the distribution of each measure as a simple barplot with errorbars: import pandas as pd import ptitprince as pt import seaborn as sns import matplotlib.pyplot as plt sns.set(style = "whitegrid" ,font_scale = 2 ) import matplotlib.collections as clt df = pd.read_csv ( "simdat.csv" , sep = "," ) sns.barplot(x = "group" , y = "score" , data = df, capsize = .1 ) This plot can give the reader a first idea of the dataset: which group has a larger mean value, and whether this difference is likely to be significant or not.
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