What are the limitations of using histograms for problem analysis?

1 Bin size and shape

One of the limitations of histograms is that they depend on the choice of bin size and shape, which can affect how the data is displayed and interpreted. If the bins are too large, you may miss some important details or variations in the data. If the bins are too small, you may see too much noise or randomness in the data. The shape of the bins can also influence the appearance of the histogram, as different shapes can create different gaps or overlaps between the bins. To overcome this limitation, you should experiment with different bin sizes and shapes until you find the one that best suits your data and your problem analysis objective. You should also compare your histogram with other descriptive statistics, such as mean, median, mode, standard deviation, and quartiles, to get a more comprehensive view of your data.

2 Data type and distribution

Another limitation of histograms is that they are not suitable for all types of data and distributions. Histograms are best for continuous or numerical data that have a single peak or mode, such as height, weight, or temperature. They are not ideal for categorical or discrete data that have multiple modes or categories, such as gender, color, or rating. They are also not effective for data that have a skewed or non-normal distribution, such as income, age, or sales. To avoid this limitation, you should use other graphical tools that are more appropriate for your data type and distribution, such as bar charts, pie charts, box plots, or scatter plots. You should also check the assumptions and requirements of your problem analysis method or technique, as some of them may require a normal distribution or a certain type of data.

3 Data quality and quantity

A third limitation of histograms is that they are sensitive to the quality and quantity of the data. Histograms can be misleading or inaccurate if the data is incomplete, incorrect, or inconsistent. For example, if the data has missing values, outliers, or errors, the histogram may not reflect the true frequency or distribution of the data. If the data is too small or too large, the histogram may not capture the variability or the significance of the data. To overcome this limitation, you should perform some data cleaning and validation before creating your histogram. You should also use some statistical tests or measures to assess the reliability and validity of your data and your histogram.

4 Data interpretation and communication

Histograms are powerful and useful tools for problem analysis, but they can be difficult to interpret and communicate, especially for non-technical audiences. To avoid this limitation, you should follow some best practices when creating and using histograms. This includes choosing a descriptive and relevant title, using consistent and appropriate units and labels for your bins and axes, selecting an appropriate scale, adding annotations to highlight or explain important features or findings, providing context and background for your data and your problem analysis objective, and using simple language to describe and summarize your histogram. By understanding and addressing these limitations, you can improve your problem analysis skills and outcomes.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?