A histogram organizes a collection of data points into ranges based on their characteristics. The histogram enables users to easily interpret data by taking numerous data points and condensing them into logical ranges. Histograms are graphs that display data in columns and buckets of the outcomes by their X-axis. This axis shows the number of occurrences or percentages for each column, which can help visualize the distribution of data. Despite their similarities, histograms and bar charts are not the same thing. It displays continuous data, while a bar chart represents categorical data. 

Some authors advise placing gaps between the rectangles to clarify the data’s meaning. This useful data collection and analysis tool is regarded as one of the seven basic quality tools. Histograms are the most commonly used graph for demonstrating frequency distributions. Histograms resemble bar charts, but there are many differences between them. Ensure the process was functioning normally during the period being studied before drawing any conclusions from the histogram. If any unusual events occurred during this period, you likely cannot use the histogram shape as a basis for a conclusion.

Tips to select the right bin width:

• You need to ensure that a bin doesn’t fall into either the large or small category, and there is no right or wrong answer as to its width. According to a histogram, the height of the bar does not necessarily reveal how many occurrences of scores there were within each bin. Instead, the width of the bar indicates the likelihood of occurrence inside a given bin.
•  A bin’s frequency of occurrences is based on the product of its height and its width. Many histograms contain equal spaces between bar heights, which means that the height of the bin does reflect the area under these circumstances, which is why height is often incorrectly thought to indicate frequency instead of area

Different types of histograms shapes:

• Normal distribution: In a normal distribution, points can appear on either side of the average as well as on the other. Other distributions look like the normal distribution. Statistics must be used to determine if a distribution is normal.
• Skewed distribution: As a result of a natural limit, an asymmetric distribution cannot have outcomes on one side. The peak of the distribution is toward the limit, while the tail extends away from it. Despite the tail being skewed either right or left, these distributions are named accordingly. 
• Bimodal distribution: It looks very similar to the back of a two-humped camel because it requires two processes with independent distributions to produce one set of data. As an example, a two-shift operation with independent distributions would be considered bimodal.
• Edge peak distribution: It resembles the normal distribution, with the exception that it often features a bump at one end. This usually occurs when the histogram is constructed incorrectly, by lumping data into a group labeled- greater than
• Comb distribution: If a histogram had been constructed incorrectly, rounded-off data and/or a comb distribution might appear. For example, if the bar width for the temperature data was larger than the bar width for the regression data, a comb distribution would appear.
• Truncated distribution: As a normal distribution with its tails removed, the truncated distribution appears normal. Suppose the supplier produces a normal distribution of materials and then uses inspection to separate what is within specifications from what is outside of specifications. The shipment to the customer within specifications is considered the core.

Histograms are popular charting tools, just like a bar chart that illustrates how the distribution of data fits into a convenient form. You can learn about both these concepts from online platforms such as Cuemath, one of the best websites for learning the fundamentals of all concepts relating to mathematics.