Table of Contents
Histograms are bar graphs that represent the distribution of a numerical variable. They are most often used to represent the frequency of occurrence of various values in a dataset. The shape of a histogram depends on the data being plotted, but typically they are bell-shaped, skewed to the right, or have multiple peaks. For example, a bell-shaped histogram would indicate that most of the data points lie around the mean, whereas a skewed to the right histogram would indicate that most of the data points are greater than the mean. Multiple peaks in a histogram can suggest multiple populations or clusters of data.
A histogram is a type of chart that allows us to visualize the distribution of values in a dataset.
The x-axis displays the values in the dataset and the y-axis shows the frequency of each value.
Depending on the values in the dataset, a histogram can take on many different shapes.
The following examples show how to describe a variety of different histograms.
1. Bell-Shaped
A histogram is bell-shaped if it resembles a “bell” curve and has one single peak in the middle of the distribution. The most common real-life example of this type of distribution is the .
2. Uniform
A histogram is described as “uniform” if every value in a dataset occurs roughly the same number of times. This type of histogram often looks like a rectangle with no clear peaks.
3. Bimodal
A histogram is described as “bimodal” if it has two distinct peaks. We often say that this type of distribution has multiple modes – that is, multiple values occur most frequently in the dataset.
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4. Multimodal
A histogram is described as “multimodal” if it has more than two distinct peaks.
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5. Left Skewed
A histogram is left skewed if it has a “tail” on the left side of the distribution. Sometimes this type of distribution is also called “negatively” skewed.
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6. Right Skewed
A histogram is right skewed if it has a “tail” on the right side of the distribution. Sometimes this type of distribution is also called “positively” skewed.
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7. Random
The shape of a distribution can be described as “random” if there is no clear pattern in the data at all.
The following tutorials provide more information on how to describe distributions.