# Interpreting Distribution in Histograms

Histograms are a type of graph that shows how data is distributed. They are often used to show things like how often something happens or how many people have a certain score on a test. Interpreting histograms can be tricky, but there are some things to look for that can help. Keep reading to learn more about how to interpret histograms.

Table of Contents

**Histogram Chart**

A **histogram chart** is a graphical representation of the distribution of data. It shows how often data occurs within certain ranges. The x-axis shows the range of values, and the y-axis shows how many data points fall into each range.

Histograms can be used to compare two or more sets of data, identify outliers, and see if the data is distributed evenly. To create a histogram, you first need to collect data and then group it into intervals. The widths of the intervals will depend on how much detail you want in your histogram.

The most common use for histograms is to display continuous data. However, they can also be used with categorical data by grouping the categories together. When using categorical data, make sure that there are enough categories so that each one has at least five observations. It is an efficient **data visualization** tool used to demonstrate the distribution of numerical data.

The histogram consists of a series of rectangles, one for each data value. The height of each rectangle is proportional to the frequency of that data value. A smooth curve, called a “histogram curve,” is drawn through the tops of the rectangles. The histogram curve is a graphical representation of the distribution of the data. It shows the shape of the distribution and the location of the median and the extremes. It can also be used to measure the variability of the data.

**Skewness and Kurtosis**

Skewness is a measure of the symmetry of a distribution. Distribution is symmetric if it looks the same on both sides of the median. If it looks more lopsided to one side, then it has negative skewness. If it looks more lopsided to the other side, then it has positive skewness. Kurtosis is a measure of how “peaked” or “flat” a distribution is. A distribution with lots of peaks and valleys will have high kurtosis. A distribution with little variation will have low kurtosis.

**Negatively Skewed Data**

A histogram that is skewed to the left indicates that the data is likely to be negatively skewed. This means that there is a greater number of data points that are at the lower end of the scale and a smaller number of data points that are at the higher end of the scale. This can be due to a variety of factors, such as a small number of extremely high or low scores skewing the average or a population that is generally unhealthy or uneducated. A histogram that is skewed to the right, on the other hand, indicates positively skewed data.

**Positively Skewed Data**

A histogram that is skewed to the right indicates positively skewed data. This means that the majority of the data points are concentrated at the higher end of the range, with a smaller number of data points at the lower end. This pattern can be caused by a few outliers that have a large impact on the overall distribution or by a genuine shift in the data that is skewing the average upwards. In either case, it is important to investigate the cause of the skew to understand the underlying trend.

**Central Tendency**

The median is a good measure of central tendency for data that is negatively skewed. This is because the median is not influenced by outliers, which are extreme values that pull the average away from the rest of the data. In a histogram, data that is negatively skewed will have a long tail on the left side, with most of the data clustered around the mean. The median will be closer to the true center of this distribution than the mean.

**Normal Distribution**

A normal distribution is a type of distribution that is symmetrical and bell-shaped. This type of distribution occurs when the variable being measured is normally distributed. A normal distribution can be described by its mean, median, and mode. The mean is the average of all of the values in the distribution, the median is the value that falls in the middle of the distribution, and the mode is the most common value in the distribution.

Interpreting distribution is important because it can help you understand how a data set is distributed. This can help you make better decisions when analyzing and interpreting data. A histogram can be used in many applications as a powerful **data analysis** tool and can help highlight data insights.