Common Visualization Mistakes and How to Avoid Them

  • ID: VISPY-FREE-11
  • Type: Lesson
  • Audience: Public
  • Theme: Diagnostic thinking in visualization

Professional visualization is not only about creating good figures.

It is about recognizing bad ones.

In this lesson, you will learn to identify and correct common mistakes:

Setup

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

from cdi_viz.theme import (
    cdi_notebook_init,
    show_and_save_mpl,
)

warnings.filterwarnings("ignore")

cdi_notebook_init(chapter="11")

df = pd.read_csv("data/cdi-student-outcomes.csv")

print("First rows:")
print(df.head())
First rows:
     group  test_prep  study_hours  math_score  reading_score  writing_score
0  Group B  completed          3.9          58             64             51
1  Group A       none          7.7          67             85             61
2  Group A       none          9.3          83             65             73
3  Group A       none          3.9          60             67             48
4  Group A       none          8.3          68             63             47

1. Truncated Axes

Truncating axes can exaggerate differences.

Misleading Version

group_means = df.groupby("test_prep")["math_score"].mean()

fig, ax = plt.subplots(figsize=(7.0, 4.6))

ax.bar(group_means.index, group_means.values)

ax.set_ylim(70, 85)  # truncated axis
ax.set_title("Average math score by test prep (misleading scale)")
ax.set_ylabel("Average math score")

fig.tight_layout()

show_and_save_mpl(fig)  # figures/11_001.png
Saved PNG → figures/11_001.png

Corrected Version

fig, ax = plt.subplots(figsize=(7.0, 4.6))

ax.bar(group_means.index, group_means.values)

ax.set_ylim(0, 100)  # full logical range
ax.set_title("Average math score by test prep (full scale)")
ax.set_ylabel("Average math score")

fig.tight_layout()

show_and_save_mpl(fig)  # figures/11_002.png
Saved PNG → figures/11_002.png

2. Overloaded Color

Too many colors reduce clarity.

fig, ax = plt.subplots(figsize=(7.6, 4.6))

sns.scatterplot(
    data=df,
    x="study_hours",
    y="math_score",
    hue="group",   # multiple categories
    style="test_prep",
    ax=ax
)

ax.set_title("Too many encodings at once")
ax.set_xlabel("Study hours per week")
ax.set_ylabel("Math score")

fig.tight_layout()

show_and_save_mpl(fig)  # figures/11_003.png
Saved PNG → figures/11_003.png

Ask yourself:

Is every encoding necessary?

3. Wrong Chart Type

Bar charts are often misused for continuous distributions.

Better:

fig, ax = plt.subplots(figsize=(7.6, 4.6))

sns.histplot(df["math_score"], bins=25, ax=ax)

ax.set_title("Distribution of math scores")
ax.set_xlabel("Math score")
ax.set_ylabel("Count")

fig.tight_layout()

show_and_save_mpl(fig)  # figures/11_004.png
Saved PNG → figures/11_004.png

Choose the chart type that matches the data structure.

4. Independent Scales in Multi-Panel Figures

Without shared scales, comparisons break.

groups = sorted(df["group"].unique())

fig, axes = plt.subplots(ncols=len(groups), figsize=(11, 4))

for ax, g in zip(axes, groups):
    sub = df[df["group"] == g]
    ax.scatter(sub["study_hours"], sub["math_score"], alpha=0.6)
    ax.set_title(f"Group {g}")
    ax.set_xlabel("Study hours")

axes[0].set_ylabel("Math score")
fig.suptitle("Independent scales can mislead", y=1.02)
fig.tight_layout()

show_and_save_mpl(fig)  # figures/11_005.png
Saved PNG → figures/11_005.png

Better:

fig, axes = plt.subplots(ncols=len(groups), figsize=(11, 4), sharey=True)

for ax, g in zip(axes, groups):
    sub = df[df["group"] == g]
    ax.scatter(sub["study_hours"], sub["math_score"], alpha=0.6)
    ax.set_title(f"Group {g}")
    ax.set_xlabel("Study hours")

axes[0].set_ylabel("Math score")
fig.suptitle("Shared scales enable valid comparison", y=1.02)
fig.tight_layout()

show_and_save_mpl(fig)  # figures/11_006.png
Saved PNG → figures/11_006.png

Diagnostic Checklist

Before finalizing a figure:

  • Is the axis scale appropriate?
  • Is color encoding meaningful?
  • Is the chart type appropriate?
  • Are scales consistent across panels?
  • Is anything exaggerating differences unintentionally?

Key Takeaways

  • Visualization errors often look subtle.
  • Axis scaling strongly affects perception.
  • Too much encoding reduces clarity.
  • Shared scales matter in comparisons.
  • Always ask: could this be misinterpreted?

Exercises

  1. Create a deliberately misleading plot. Then correct it.
  2. Remove one encoding from a cluttered scatter plot.
  3. Compare a bar chart vs histogram for continuous data.
  4. Build a multi-panel figure with and without shared scales and compare interpretation.