Learn Bar and Box Plots for Group Comparisons | Visualizing Research Results

Comparing groups is a fundamental part of research analysis, and two of the most effective ways to visualize these comparisons are bar plots and box plots. Bar plots are best for showing differences in summary statistics—like mean or median—between groups, making it easy to compare central tendencies at a glance. In contrast, box plots provide a more detailed picture of the data's distribution within each group, letting you quickly spot outliers and understand the spread of your measurements. Choosing between these plots depends on whether you want to highlight group averages or reveal more about the underlying data patterns and variability.


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import matplotlib.pyplot as plt
import pandas as pd

# Sample data: mean test scores for two groups
data = {
    "Group": ["Control", "Experimental"],
    "Mean_Score": [72, 85]
}
df = pd.DataFrame(data)

plt.bar(df["Group"], df["Mean_Score"], color=["gray", "blue"])
plt.ylabel("Mean Score")
plt.title("Mean Test Scores by Group")
plt.show()

While bar plots focus on summarizing a single statistic—such as the mean—box plots go further by displaying the full distribution of the data. A box plot shows the median, the interquartile range, and highlights any outliers, giving you a sense of both the typical values and the variability within each group. This makes box plots especially useful when you want to compare the spread, symmetry, or presence of unusual values between research groups.


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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data: measurements for two groups
data = {
    "Group": ["Control"] * 8 + ["Experimental"] * 8,
    "Measurement": [68, 70, 72, 74, 75, 71, 69, 73, 82, 85, 88, 90, 86, 84, 89, 87]
}
df = pd.DataFrame(data)

sns.boxplot(x="Group", y="Measurement", data=df, palette=["gray", "blue"])
plt.title("Measurement Distribution by Group")
plt.show()

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Section 2. Chapter 2

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              1234567891011121314
            
import matplotlib.pyplot as plt
import pandas as pd

# Sample data: mean test scores for two groups
data = {
    "Group": ["Control", "Experimental"],
    "Mean_Score": [72, 85]
}
df = pd.DataFrame(data)

plt.bar(df["Group"], df["Mean_Score"], color=["gray", "blue"])
plt.ylabel("Mean Score")
plt.title("Mean Test Scores by Group")
plt.show()


              1234567891011121314
            
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data: measurements for two groups
data = {
    "Group": ["Control"] * 8 + ["Experimental"] * 8,
    "Measurement": [68, 70, 72, 74, 75, 71, 69, 73, 82, 85, 88, 90, 86, 84, 89, 87]
}
df = pd.DataFrame(data)

sns.boxplot(x="Group", y="Measurement", data=df, palette=["gray", "blue"])
plt.title("Measurement Distribution by Group")
plt.show()

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Bar and Box Plots for Group Comparisons

1. What does a box plot show that a bar plot does not?

2. Which seaborn function is used to create a box plot?

3. When would you use a bar plot in research?

Bar and Box Plots for Group Comparisons

1. What does a box plot show that a bar plot does not?

2. Which seaborn function is used to create a box plot?

3. When would you use a bar plot in research?