Box and Whisker Plot Maker

  • Generates Box and Whisker Plot
  • Calculates Key Quartile Statistics

Box and Whisker Plot

Key Statistics

Sample has 28 observations.

25th Percentile: 3.0000
Sample Median: 3.5000
75th Percentile: 5.2500
Interquartile Range: 2.2500
Maximum: 26.0000

More Statistics (Save Data First)


Click To Clear; enter values seperated by commas or new lines. Cut & Paste from Excel also works.

Can be comma separated or one line per data point; you can also cut and paste from Excel.

Saved Datasets - Click to Restore

Saved in your browser; you can retrieve these and use them in other calculators on this site.

Sharing Results of The Box and Whisker Plot Maker

Need to pass an answer to a friend? It's easy to link and share the results of this calculator. Hit calculate - then simply cut and paste the url after hitting calculate - it will retain the values you enter so you can share them via email or social media.

Using The Box and Whisker Plot Maker

Enter your data as a string of numbers, separated by commas. Then hit calculate. The Box and Whisker Plot Maker will generate a list of key measures and make a box plot chart to show the distribution.

For easy entry, you can copy and paste your data into the box plot maker from Excel. You can save your data for use with this calculator and other calculators on this site. Just hit the "save data" button. It will save the data in your browser (not on our server, it remains private). Saved data sets will appear on the list of saved datasets below the data entry panel. To retrieve it, click the "load data" button next to it. This is useful if you want to tweak the data and rerun it in the box and whisker calculator or pass it another statistical tool.

Explore Single Series

Explore (X,Y) Pairs

Test Single Sample

Compare Samples

Confidence Intervals

Basic Probability

Probability Distributions

Classical Statistics

Master Box and Whisker Plot Maker: Unleash the Power of Data Visualization

In the world of data analysis, visual representations are crucial for understanding complex data sets and patterns. One common visual tool used to display and analyze numerical data is the box and whisker plot, also known as a whisker chart or box plot. This type of chart is highly effective for displaying the distribution of a given set of data, with particular emphasis on the median, quartiles, and extreme values. Box plots are widely used in various fields, including statistics, business analytics, and scientific research to gain insights from data.

Creating a box and whisker plot may seem like a daunting task, especially for those new to data visualization. However, with the right tools and guidance, it can be a straightforward and even enjoyable process. There are various ways to create such plots, including using popular spreadsheet programs like Google Sheets or Excel. These programs allow users to input their data and format data series to generate a whisker chart complete with all relevant details, such as the upper and lower quartiles, median value, and interquartile range.

However, a box and whisker plot maker website can greatly simplify the process for those seeking a more user-friendly and efficient option. With an intuitive interface, these plot generators can help you create visually appealing and accurate box plots by following a few simple steps. Just import your data set, specify values, and watch as the plot maker automatically calculates the quartiles, maximum and minimum values, and any other relevant insights you need. This makes it even easier for both beginners and experienced professionals to create detailed, comprehensible whisker plots for their data analysis needs.

Box and Whisker Plot

A box and whisker plot, also referred to as a box plot or whisker chart, is a powerful graphical method for depicting the distribution and variation of a given data set. This type of plot is particularly useful for visualizing the central tendencies and dispersion of numerical data in a concise and straightforward manner.

Definition of Box and Whisker Plot

A box and whisker plot consists of a rectangular box representing the interquartile range (IQR), which is the range between the first quartile (25th percentile) and the third quartile (75th percentile) of a data set. The middle line inside the box signifies the median value. Extending from each end of the box are whiskers, which indicate the smallest and largest values within the data set that are not considered outliers. Lastly, any outliers are usually represented as individual points or symbols outside the whiskers.

Explanation of the Components of a Box and Whisker Plot

Benefits of Using Box and Whisker Plot as a Visual Representation of Data Distribution

Box and whisker plots offer numerous benefits for visually representing data distribution, including the ability to:

In conclusion, box and whisker plots serve as a valuable tool for analyzing and visualizing data distributions. They offer a concise and easy-to-understand representation of central tendency, spread, and overall range, making them a popular choice for presenting complex statistical data to broad audiences.

How to Create a Box and Whisker Plot

Box and whisker plots are a popular type of statistical chart that visually displays the distribution and variability of a given data set. They are particularly useful for comparing different data sets and identifying patterns, outliers, and other characteristics of the data. In this section, we will discuss how to create a box and whisker plot using a box plot generator or Excel and provide tips for formatting and customizing the chart.

Explanation of How to Create a Box and Whisker Plot Using a Box Plot Generator or Excel

Creating a box and whisker plot can be done easily with online tools like a box plot generator or with software like Excel. Both options allow you to input your data, and the tool will automatically generate the plot for you. The key components of the box and whisker plot include the lower quartile (1st quartile), median (2nd quartile), upper quartile (3rd quartile), minimum value, and maximum value, often represented as a five-number summary. These values divide the data into four equal parts or quartiles.

Box and whisker plots can be created in Excel by selecting the appropriate chart type from the chart menu. Google Sheets also offers a similar option for creating these plots. It is essential to ensure that the data is correctly formatted and sorted before creating the chart to avoid errors or skewed results.

Steps to Format Data Series and Specify Values for Each Quartile and Whisker

  1. Organize your data: Begin by organizing your numerical data in a column or row within your spreadsheet application. If using Excel or Google Sheets, make sure your data is sorted in ascending order.
  2. Select the data: Highlight the entire data series that you want to include in the box and whisker plot.
  3. Insert a chart: Navigate to the chart menu (in Excel, it is under the "Insert" tab), and select the Box and Whisker chart type from the list of available options.
  4. Format the data series: The software will automatically calculate and display the quartile values and whiskers' positions. You can customize these values by right-clicking on the data series in the chart and selecting "Format Data Series" from the context menu.
  5. Specify custom values: Within the format options, you can adjust the whiskers' length, set them to display at specific percentiles or use custom values for the quartiles and whisker endpoints.

Tips for Creating an Effective Box and Whisker Plot

To create a clear and informative box and whisker plot, follow these tips:

A well-designed box and whisker plot can provide valuable insights into the patterns, variability, and distribution of your data, and serves as a useful tool for exploratory data analysis and comparisons. By following the steps and tips outlined above, you will be able to create clear, effective, and informative box and whisker plots.

Interpretation of Box and Whisker Plot

A box and whisker plot, also known as a whisker chart or simply a box plot, is a powerful visual representation tool used in statistical data analysis. It provides a quick summary of a given dataset's distribution and helps identify patterns, outliers, and trends. This section will cover the interpretation of box and whisker plots, identifying outliers, and comparing box plots to other data visualization tools.

Explanation of How to Interpret a Box and Whisker Plot

Box and whisker plots are composed of several elements, including the median, quartiles, whiskers, and outliers. The following is a brief explanation of these elements:

To interpret a box and whisker plot, start by examining the overall shape of the box and the position of the median line. The wider the box, the more data variation there is within that middle 50% of the dataset. A higher median indicates that the data's central tendency is toward higher values.

Analyze the whiskers to understand the range and spread of values outside the IQR. Short whiskers show a smaller range of variation, while longer whiskers imply a broader range. Lastly, note any outliers to determine if they have a significant impact on the dataset's trends and distribution.

Identification of Outliers and Their Impact on the Data Set

Outliers are data points that fall well outside the other values in a dataset. In a box and whisker plot, outliers can be identified by analyzing any data points that lie beyond the whiskers. The impact of outliers on the dataset can vary, as they may skew the data's symmetry or influence the mean and standard deviation of the data set.

It is essential to recognize outliers and understand their impact on the dataset as they can lead to incorrect interpretations and misinform critical decisions based on said data. If the presence of outliers is justified and reasonable within the context of the data, they should be included in the analysis. In contrast, if outliers are deemed erroneous or irrelevant, it might be necessary to re-evaluate the dataset or consider alternative methods for data analysis.

Comparison of Box and Whisker Plot to Other Statistical Data Visualization Tools

Box and whisker plots are not the only statistical data visualization tools available. Other popular techniques include bar charts, column charts, histograms, and stacked column charts. Here is a brief comparison between these methods:

In conclusion, box and whisker plots are highly efficient tools for visually exploring data distribution, identifying trends, and detecting outliers in a given dataset. They provide a compact representation of data sets, making them ideal for comparing multiple datasets and identifying variations among different data groups. When used in conjunction with other data visualization tools such as bar charts, histograms, and column charts, box and whisker plots can provide invaluable insights into the world of data analysis.

Examples of Box and Whisker Plot Maker

In this section, we will explore different examples of using a box and whisker plot maker to analyze various types of data, such as test scores, survey results, and financial data. The box and whisker plot is a powerful tool for visualizing the distribution of numerical data and quickly identifying key statistics, such as median values, quartiles, and extremes.

Demonstration of How to Create a Box and Whisker Plot Using Google Sheets or Other Box and Whisker Plot Maker Tools

Creating a box and whisker plot in Google Sheets or another plot maker tool is typically a straightforward process. First, collect your data and enter it into a spreadsheet or other compatible format. Make sure to organize your data into columns for easy visualization.

Next, select the data you wish to plot and choose the box and whisker plot option from the chart or plot type menu. This will generate a plot with vertical or horizontal lines, boxes, and whiskers representing the data distribution. You can customize the appearance of the plot, such as colors and axis labels, as needed.

In Statistics Kingdom's advanced boxplot maker, you can take advantage of additional customization options, including size, colors, min, max, and more. This allows for even more precise control over your plot's appearance and focus.

Examples of How Box and Whisker Plot Maker Can Be Used to Analyze Numerical Data

Box and whisker plots can be used to analyze a diverse range of numerical data, from test scores to financial data. Here are a few examples:

Test Scores

Box and whisker plots can help visualize the distribution of test scores for a class or group. For example, a teacher might use a plot to compare the scores of different sections or subjects within a given semester. By plotting the median, quartiles, and extreme values, educators can quickly see which subjects were more challenging or easier for students and identify any significant outliers.

Survey Results

Survey data often includes numerical values, such as ratings or rankings, that can be visualized using a box and whisker plot. This can help identify trends and patterns in the responses as well as any extreme or unusual results. For example, a company might use a box and whisker plot to analyze customer satisfaction ratings, allowing them to quickly identify areas for improvement or exceptional performance.

Financial Data

Box and whisker plots can be useful for visualizing financial data, such as stock prices or revenues. By plotting the median, quartiles, and extremes, analysts can quickly see trends and variations in the data, as well as any outliers. This can be particularly helpful for comparing investment options, analyzing financial performance over time, and more.

In conclusion, the box and whisker plot maker is a versatile and powerful tool for exploring and understanding numerical data in various fields, from education to finance. By using Google Sheets or other specialized tools, users can easily create these plots and uncover valuable insights for data-driven decision-making.


In this article, we discussed using a box and whisker plot maker website to create whisker plots, box plots, and whisker charts. These visual tools are beneficial for understanding data sets and conducting exploratory data analysis. We covered important aspects of these plots, such as formatting data series, understanding quartiles, and interpreting statistical information from the chart. Now, let's summarize the key concepts and discuss the importance of using a box and whisker plot maker as a powerful tool for data visualization and exploratory data analysis.

Summary of the Key Concepts and Ideas Related to Box and Whisker Plot Maker

Box and whisker plots provide a visual representation of the data distribution of a given set by breaking down the data into quartiles. The plot represents the minimum value, lower quartile (1st quartile), median, upper quartile (3rd quartile), and maximum value by showing the spread of the data and revealing possible outliers. The interquartile range (IQR) highlights the middle 50% of the data, which is the range between the 1st and 3rd quartiles. Using a box and whisker plot maker can simplify the process of creating these plots and help visualize large datasets efficiently.

Box and whisker plots can be generated using various tools, such as Excel, Google Sheets, and dedicated box plot generator websites. These tools allow users to customize the appearance of the plots by specifying values for axes, adjusting colors, shapes, and more. Properly formatting data series helps improve data visualization and readability for users.

Importance of Using Box and Whisker Plot Maker as a Powerful Tool for Data Visualization and Exploratory Data Analysis

Box and whisker plots are essential tools in scientific research, as they offer a compact, visual summary of complex data sets. With information on central tendencies and dispersion, these plots reveal the symmetry and skewness of the data. Using a box and whisker plot maker allows users to quickly create and analyze these plots for a better understanding of the data distribution.

In addition to visualizing data, box and whisker plots provide insights into the nature of the data and guide further analysis. By identifying potential outliers and data trends, researchers can focus on specific data points or groups for in-depth examination. Considering the ease of use and the valuable insights they provide, box and whisker plot makers are indispensable tools for data analysis and visualization.

Overall, using a box and whisker plot maker website can significantly simplify the process of creating and interpreting these plots, making them more accessible and useful in a variety of disciplines. By leveraging the power of these tools and understanding the concepts discussed in this article, you can unlock the potential of box and whisker plots for your data analysis needs.

The Box and Whisker Plot Maker has two key purposes:

The box and whisker plot is a common visual tool used for exploratory data analysis. This calculator is designed to make it quick and easy to generate a box and whiskers plot and associated descriptive statistics. Simple enter your data into the Box and Whisker Plot Maker and you will get a quick view of the shape of the distribution.

Interpreting a Box and Whisker Plot

The core of the distribution is shown by the box in the plot. This represents the gap between the 25th percentile and the 75th percentile of the distribution, generally referred to as the interquartile range. This is usually a relatively dense region. This is particularly true if the data generated by the underlying process is normally distributed. The upper and lower lines denote the maximum and minimum values observed in the sample data. This gives you perspective on the outliers of a particular sample. Want to learn more about analyzing box plots? Statistics Canada published a good overview of using this statistical tool. Additional materials can be found here.

Finding the Range of a Set of Numbers

This Box and Whisker Plot Maker is particularly useful if you need to find the range of a set of numbers and analyze it. The box plot generator provides both the interquartile range (distance between 25th and 75th percentile) and the total range (min vs. max) of a data sample.

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