Choosing the Right Graph: How to Visualize Your Data in Statistics
When you collect data, your next big step is to understand it — and the best way to do that is to visualize it.
But not every graph works for every type of data.
In this post, we’ll explore:
- How to choose a graph based on your data type
- The difference between bar charts, pie charts, and histograms
- The different shapes of histograms (and what they tell us)
📚 This post is part of the "Intro to Statistics" series
🔙 Previously: Descriptive vs Inferential Statistics
🔜 Next: Choosing the Right Graph: How to Visualize Your Data
📋 1. Categorical Data: Nominal and Ordinal
Categorical data includes labels, names, or categories.
There are two types:
- Nominal: No order (e.g., eye color, favorite food)
- Ordinal: Ordered categories (e.g., rating from poor to excellent)
🔹 Best graphs for categorical data:
Bar Chart
- Each category is a separate bar
- Bar height = frequency
- Bars are separated (not touching)
Pie Chart
- Shows parts of a whole
- Best when you want to show percentages or proportions
💡 Tip: Bar charts are usually easier to read than pie charts — especially with many categories.
📊 2. Quantitative Data: Interval and Ratio
Quantitative data is numerical, like height, age, or test scores.
This includes:
- Interval: No true zero (e.g., temperature in °C)
- Ratio: Has a true zero (e.g., weight, age)
🔸 Best graph for quantitative data:
Histogram
- Bins or intervals group data (e.g., ages 10–19, 20–29, etc.)
- Bars are connected to show continuous data
🧠 3. Types of Histogram Shapes
Histograms don’t just show data — their shapes tell a story.
📈 Bell-Shaped (Normal)
- Most data is in the center
- Few values at the extremes
- Example: IQ scores, height
🔄 Skewed Right (Positive Skew)
- Long tail on the right
- Most values are low
- Example: Income (most people earn little, few earn a lot)
🔃 Skewed Left (Negative Skew)
- Long tail on the left
- Most values are high
- Example: Age of retirement (most people retire around 60–65)
⛰️ Bimodal (Two Peaks)
- Two distinct groups in the data
- Example: Test scores from two different classes
🖼️ Visual Guide to Histogram Shapes
🧠 Level Up: Why Choosing the Right Graph Matters in Data Science
Effective data visualization is more than just making charts look nice — it’s about choosing the right tool to reveal insights clearly and accurately:
- 📊 Bar charts and pie charts work best for categorical data, helping compare groups or parts of a whole.
- 📈 Histograms are ideal for quantitative data, showing distribution shapes like normal, skewed, or bimodal.
- 🔍 The shape of a histogram can hint at underlying processes, identify outliers, and guide statistical modeling decisions.
- 🎯 Choosing the wrong graph can mislead viewers or hide important patterns — so the choice of graph is a vital skill.
Mastering graph selection will make your analyses clearer and your communication more impactful.
📌 Try It Yourself
Q: You surveyed 200 people on their favorite social media platform. Which graph is most appropriate for showing your results?
💡 Show Answer
✅ A bar chart or pie chart — because you’re dealing with a categorical (nominal) variable.
Each bar (or slice) represents the count or percentage for a platform like Instagram, TikTok, or X (Twitter).
Bonus: What if instead you had their screen time in hours?
💡 Show Answer
✅ Then use a histogram — because the data is quantitative and continuous.
You can group the screen time into intervals (e.g., 0–2, 2–4 hours) and visualize the distribution.
🧾 Summary Table
| Data Type | Graph Type | Use When… |
|---|---|---|
| Nominal | Bar, Pie | Categories with no order |
| Ordinal | Bar | Ordered categories |
| Interval/Ratio | Histogram | Numeric data (continuous) |
✅ Up Next
We’ll build on this and create frequency tables — the building blocks behind many of these graphs!