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Module 1 · Descriptive Statistics · Topic 7

Data Visualization Basics

Why Visualize Data?. Visualizations reveal patterns, trends, and outliers that raw numbers alone might hide. A good chart communicates the story in the data quickly and clearly.

Common Chart Types. Histograms show frequency distributions of quantitative data. Bar charts display counts or percentages for categorical data. Box plots summarize the five-number summary. Dot plots show individual data points along a number line.

Histograms vs Bar Charts. Histograms have adjacent bars (no gaps) because the x-axis is a continuous scale. Bar charts have gaps between bars because each bar represents a separate category.

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Key formulas

From the Descriptive Statistics formula sheet

  • Mean: xbar = sum(x_i) / n — The arithmetic average of a data set.
  • Sample Variance: s^2 = sum((x_i - xbar)^2) / (n - 1) — Average squared distance from the sample mean with Bessel correction.

See the full formula reference

Worked example

Choosing the Right Display

To show the distribution of test scores for 200 students, use a histogram. To compare the number of students in each major, use a bar chart. To compare score distributions across three classes, use side-by-side box plots.

Related glossary terms

  • Outlier: A data point that is unusually far from the rest of the data. Example: In 2, 4, 5, 6, 50, the value 50 is a likely outlier.

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Related in-depth guides

  • Understanding Standard Deviation: What It Measures and Why It Matters7 min read
  • How to Choose the Right Statistical Test8 min read

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