In data analysis, histograms are a simple yet very effective visualization tool to understand the distribution of datasets. Interpreting histograms is therefore key not only to get insights on the data distribution, but also to identify patterns and make decisions on which methods to apply for further analysis. If interpreted correctly, the shape of the histograms uncovers important characteristics about your data distribution, like symmetry, presence of atypical values, and dispersion, to name a few.
This article provides an example-based guide to describe and understand your data based on their histogram shape, that is, the underlying distribution of the data.
Types of Histogram Forms and Their Interpretation
Let’s introduce six example histograms with different shapes to gain a comprehensive understanding of what they are saying about the data.
1. Normal or symmetric histogram
These are bell-shaped histograms, visually resembling a normal or Gaussian distribution. This shape is an indicator of most of the data being predominantly grouped around a mean value, with extreme values or cases being less frequent. This typically occurs for instance when collecting data about people’s height or weight from a large population.
2. Positively skewed histogram
When histograms have a “long tail” to their right side, this indicates that the majority of the data concentrates around the lower range of values, but there exist a few cases with higher values. This is a common pattern in financial or economic data like household income in a population: only a small minority of individuals may have a larger income.
3. Negatively skewed histogram
At the other end of the spectrum, there are histograms depicting data distributions with a “long tail” to the left, meaning that the majority of values are near the maximum, whereas a small minority are closer to the minimum. Imagine you approach a thousand retired citizens and collect data about their age of retirement: if you visually represent the distribution of these data using a histogram, you’ll most likely come across a shape like this.
4. Uniformly distributed histogram
A uniform histogram shows similar frequencies across the whole interval or range of values of your data: it is a sign that all values have roughly the same probability of occurrence. This is frequent in data generated at random without any defined trend, or when rolling a die numerous times and collecting results, but interestingly this pattern is not as common in real data and phenomena as one might think.
5. Bimodal histogram
Just like a landscape with two tall mountains next to one another, with a valley in between, a bimodal distribution in a histogram indicates the presence of two differentiated subgroups in the dataset, each normally distributed around a most frequent value or mode. An example situation where data frequencies obey this pattern occurs when analyzing data that combine different categories, like collecting exam results from two groups of students, one of which did not prepare at all and the other who did prepare thoroughly.
6. Multimodal histogram
When multiple “peaks and valleys” are shown in the histogram, there may be several well-defined subcategories in the data. Each peak indicates a most frequent value around which a data subgroup revolves. In political surveys where the opinions of several ideological groups with strong preferences are collected, it is not uncommon to come across histograms shaped like this.
Wrapping Up
Understanding the shape of histograms is crucial to correctly interpret your datasets and make informed decisions about further analyses needed. This article illustrated through six example histograms that the form of these data plots -be it symmetric, skewed, uniform, bimodal, or multimodal- sheds light on the unique nature and structure of the data and their distribution.
