Difference Between Bar Chart and Histogram

 

Bar Graph

Bar graphs are the pictorial representation of data (generally grouped), in the form of vertical or horizontal rectangular bars, where the length of bars are proportional to the measure of data. They are also known as bar charts. Bar graphs are one of the means of data handling in statistics.

The collection, presentation, analysis, organization, and interpretation of observations of data are known as statistics. The statistical data can be represented by various methods such as tables, bar graphs, pie charts, histograms, frequency polygons, etc. In this article, let us discuss what is a bar chart, different types of bar graphs, uses, and solved examples.

able of Contents:

What is Bar Graph?

The pictorial representation of grouped data, in the form of vertical or horizontal rectangular bars, where the lengths of the bars are equivalent to the measure of data, are known as bar graphs or bar charts.

The bars drawn are of uniform width, and the variable quantity is represented on one of the axes. Also, the measure of the variable is depicted on the other axes. The heights or the lengths of the bars denote the value of the variable, and these graphs are also used to compare certain quantities. The frequency distribution tables can be easily represented using bar charts which simplify the calculations and understanding of data.

The three major attributes of bar graphs are:

  • The bar graph helps to compare the different sets of data among different groups easily.
  • It shows the relationship using two axes, in which the categories on one axis and the discrete values on the other axis.
  • The graph shows the major changes in data over time.

Histogram

In statistics, a histogram is a graphical representation of the distribution of data. The histogram is represented by a set of rectangles, adjacent to each other, where each bar represent a kind of data. Statistics is a stream of mathematics that is applied in various fields. When numerals are repeated in statistical data, this repetition is known as Frequency and which can be written in the form of a table, called a frequency distribution. A Frequency distribution can be shown graphically by using different types of graphs and a Histogram is one among them. In this article, let us discuss in detail about what is a histogram, how to create the histogram for the given data, different types of the histogram, and the difference between the histogram and bar graph in detail.

Table of Contents:

What is Histogram?

A histogram is a graphical representation of a grouped frequency distribution with continuous classes. It is an area diagram and can be defined as a set of rectangles with bases along with the intervals between class boundaries and with areas proportional to frequencies in the corresponding classes. In such representations, all the rectangles are adjacent since the base covers the intervals between class boundaries. The heights of rectangles are proportional to corresponding frequencies of similar classes and for different classes, the heights will be proportional to corresponding frequency densities.

In other words, a histogram is a diagram involving rectangles whose area is proportional to the frequency of a variable and width is equal to the class interval.

How to Plot Histogram?

You need to follow the below steps to construct a histogram.

  1. Begin by marking the class intervals on the X-axis and frequencies on the Y-axis.
  2. The scales for both the axes have to be the same.
  3. Class intervals need to be exclusive.
  4. Draw rectangles with bases as class intervals and corresponding frequencies as heights.
  5. A rectangle is built on each class interval since the class limits are marked on the horizontal axis, and the frequencies are indicated on the vertical axis.
  6. The height of each rectangle is proportional to the corresponding class frequency if the intervals are equal.
  7. The area of every individual rectangle is proportional to the corresponding class frequency if the intervals are unequal.

Although histograms seem similar to graphs, there is a slight difference between them. The histogram does not involve any gaps between the two successive bars.

When to Use Histogram?

The histogram graph is used under certain conditions. They are:

  • The data should be numerical.
  • A histogram is used to check the shape of the data distribution. 
  • Used to check whether the process changes from one period to another.
  • Used to determine whether the output is different when it involves two or more processes.
  • Used to analyse whether the given process meets the customer requirements.

Difference Between Bar Graph and Histogram

A histogram is one of the most commonly used graphs to show the frequency distribution. As we know that the frequency distribution defines how often each different value occurs in the data set. The histogram looks more similar to the bar graph, but there is a difference between them. The list of differences between the bar graph and the histogram is given below:

HistogramBar Graph
It is a two-dimensional figureIt is a one-dimensional figure
The frequency is shown by the area of each rectangleThe height shows the frequency and the width has no significance.
It shows rectangles touching each otherIt consists of rectangles separated from each other with equal spaces.

The above differences can be observed from the below figures:

Bar Graph (Gaps between bars)

Histogram and Bar graph

Histogram (No gaps between bars)

Histogram

Types of Histogram

The histogram can be classified into different types based on the frequency distribution of the data. There are different types of distributions, such as normal distribution, skewed distribution, bimodal distribution, multimodal distribution, comb distribution, edge peak distribution, dog food distribution, heart cut distribution, and so on. The histogram can be used to represent these different types of distributions. The different types of a histogram are:

  • Uniform histogram
  • Symmetric histogram
  • Bimodal histogram
  • Probability histogram 

Uniform Histogram

Uniform histogram

A uniform distribution reveals that the number of classes is too small, and each class has the same number of elements. It may involve distribution that has several peaks.

Bimodal Histogram

Bimodal histogram

If a histogram has two peaks, it is said to be bimodal. Bimodality occurs when the data set has observations on two different kinds of individuals or combined groups if the centers of the two separate histograms are far enough to the variability in both the data sets.

Symmetric Histogram

Symmetric histogram

A symmetric histogram is also called a bell-shaped histogram. When you draw the vertical line down the center of the histogram, and the two sides are identical in size and shape, the histogram is said to be symmetric. The diagram is perfectly symmetric if the right half portion of the image is similar to the left half. The histograms that are not symmetric are known as skewed.

Probability Histogram

A Probability Histogram shows a pictorial representation of a discrete probability distribution. It consists of a rectangle centered on every value of x, and the area of each rectangle is proportional to the probability of the corresponding value. The probability histogram diagram is begun by selecting the classes. The probabilities of each outcome are the heights of the bars of the histogram.

Applications of Histogram

The applications of histograms can be seen when we learn about different distributions.

Normal Distribution

The usual pattern that is in the shape of a bell curve is termed normal distribution. In a normal distribution, the data points are most likely to appear on a side of the average as on the other. It is to be noted that other distributions appear the same as the normal distribution. The calculations in statistics are utilised to prove a distribution that is normal. It is required to make a note that the term “normal” explains the specific distribution for a process. For instance, in various processes, they possess a limit that is natural on a side and will create distributions that are skewed. This is normal which means for the processes, in the case where the distribution isn’t considered normal.

Skewed Distribution

The distribution that is skewed is asymmetrical as a limit which is natural resists end results on one side. The peak of the distribution is the off-center in the direction of the limit and a tail that extends far from it. For instance, a distribution consisting of analyses of a product that is unadulterated would be skewed as the product cannot cross more than 100 per cent purity. Other instances of natural limits are holes that cannot be lesser than the diameter of the drill or the call-receiving times that cannot be lesser than zero. The above distributions are termed right-skewed or left-skewed based on the direction of the tail.

Multimodal Distribution

The alternate name for the multimodal distribution is the plateau distribution. Various processes with normal distribution are put together. Since there are many peaks adjacent together, the tip of the distribution is in the shape of a plateau.


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