⭐ Histogram vs. Density Plot
Both visualize distributions, but they answer slightly different questions and behave differently.
📊 Histogram
A histogram groups data into bins and shows counts (or proportions) in each bin.
Key features
- Looks like bars
- Height = number (or proportion) of observations in each bin
- Bin width matters — changing it can change the shape
- Shows the raw structure of the data
- Can look “blocky” or jagged
When it’s useful
- When you want to see the actual frequencies
- When teaching beginners about distributions
- When the sample size is small or moderate
Example interpretation
“A lot of values fall between 10 and 20, and almost none above 40.”
📈 Density Plot
A density plot is a smoothed curve that estimates the underlying distribution.
Key features
- Smooth, continuous curve
- Area under the curve = 1
- Uses a smoothing parameter (bandwidth)
- Does not show raw counts
- Great for comparing multiple distributions
When it’s useful
- When you want to see the overall shape without binning artifacts
- When comparing groups (e.g., male vs female heights)
- When the sample size is large
Example interpretation
“The distribution is unimodal and slightly right‑skewed.”
🧠 The Core Difference
| Feature | Histogram | Density Plot |
|---|---|---|
| Shows raw counts? | Yes | No |
| Smooth? | No | Yes |
| Depends on bin width? | Yes | No (but depends on bandwidth) |
| Good for small samples? | Yes | Less so |
| Good for comparing groups? | Okay | Excellent |
🎨 Intuition
- A histogram is like stacking blocks into bins.
- A density plot is like draping a smooth blanket over the blocks to reveal the overall shape.
Both are telling the same story — one is pixelated, the other is polished.