different types of data presentation in statistics

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Data Presentation - Types & Its Importance

What is data presentation.

Data Analysis and Data Presentation have a practical implementation in every possible field. It can range from academic studies, commercial, industrial and marketing activities to professional practices.

In its raw form, data can be extremely complicated to decipher and in order to extract meaningful insights from the data, data analysis is an important step towards breaking down data into understandable charts or graphs.

Data analysis tools used for analyzing the raw data which must be processed further to support N number of applications.

Therefore, the processes or analyzing data usually helps in the interpretation of raw data and extract the useful content out of it. The transformed raw data assists in obtaining useful information.

Once the required information is obtained from the data, the next step would be to present the data in a graphical presentation.

The presentation is the key to success. Once the information is obtained the user transforms the data into a pictorial Presentation so as to be able to acquire a better response and outcome.

Methods of Data Presentation in Statistics

1. pictorial presentation.

It is the simplest form of data Presentation often used in schools or universities to provide a clearer picture to students, who are better able to capture the concepts effectively through a pictorial Presentation of simple data.

2. Column chart

It is a simplified version of the pictorial Presentation which involves the management of a larger amount of data being shared during the presentations and providing suitable clarity to the insights of the data.

3. Pie Charts

Pie charts provide a very descriptive & a 2D depiction of the data pertaining to comparisons or resemblance of data in two separate fields.

4. Bar charts

A bar chart that shows the accumulation of data with cuboid bars with different dimensions & lengths which are directly proportionate to the values they represent. The bars can be placed either vertically or horizontally depending on the data being represented.

5. Histograms

It is a perfect Presentation of the spread of numerical data. The main differentiation that separates data graphs and histograms are the gaps in the data graphs.

6. Box plots

Box plot or Box-plot is a way of representing groups of numerical data through quartiles. Data Presentation is easier with this style of graph dealing with the extraction of data to the minutes of difference.

Map Data graphs help you with data Presentation over an area to display the areas of concern. Map graphs are useful to make an exact depiction of data over a vast case scenario.

All these visual presentations share a common goal of creating meaningful insights and a platform to understand and manage the data in relation to the growth and expansion of one’s in-depth understanding of data & details to plan or execute future decisions or actions.

Importance of Data Presentation

Data Presentation could be both can be a deal maker or deal breaker based on the delivery of the content in the context of visual depiction.

Data Presentation tools are powerful communication tools that can simplify the data by making it easily understandable & readable at the same time while attracting & keeping the interest of its readers and effectively showcase large amounts of complex data in a simplified manner.

If the user can create an insightful presentation of the data in hand with the same sets of facts and figures, then the results promise to be impressive.

There have been situations where the user has had a great amount of data and vision for expansion but the presentation drowned his/her vision.

To impress the higher management and top brass of a firm, effective presentation of data is needed.

Data Presentation helps the clients or the audience to not spend time grasping the concept and the future alternatives of the business and to convince them to invest in the company & turn it profitable both for the investors & the company.

Although data presentation has a lot to offer, the following are some of the major reason behind the essence of an effective presentation:-

• Many consumers or higher authorities are interested in the interpretation of data, not the raw data itself. Therefore, after the analysis of the data, users should represent the data with a visual aspect for better understanding and knowledge.
• The user should not overwhelm the audience with a number of slides of the presentation and inject an ample amount of texts as pictures that will speak for themselves.
• Data presentation often happens in a nutshell with each department showcasing their achievements towards company growth through a graph or a histogram.
• Providing a brief description would help the user to attain attention in a small amount of time while informing the audience about the context of the presentation
• The inclusion of pictures, charts, graphs and tables in the presentation help for better understanding the potential outcomes.
• An effective presentation would allow the organization to determine the difference with the fellow organization and acknowledge its flaws. Comparison of data would assist them in decision making.

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Types of Data in Statistics – A Comprehensive Guide

• September 15, 2023

Statistics is a domain that revolves around the collection, analysis, interpretation, presentation, and organization of data. To appropriately utilize statistical methods and produce meaningful results, understanding the types of data is crucial.

In this Blog post we will learn

• Qualitative Data (Categorical Data) 1.1. Nominal Data: 1.2. Ordinal Data:
• Quantitative Data (Numerical Data) 2.1. Discrete Data: 2.2. Continuous Data:
• Time-Series Data:

Let’s explore the different types of data in statistics, supplemented with examples and visualization methods using Python.

1. Qualitative Data (Categorical Data)

We often term qualitative data as categorical data, and you can divide it into categories, but you cannot measure or quantify it.

1.1. Nominal Data:

Nominal data represents categories or labels without any inherent order, ranking, or numerical significance as a type of categorical data. In other words, nominal data classifies items into distinct groups or classes based on some qualitative characteristic, but the categories have no natural or meaningful order associated with them.

Key Characteristics

No Quantitative Meaning: Unlike ordinal, interval, or ratio data, nominal data does not imply any quantitative or numerical meaning. The categories are purely qualitative and serve as labels for grouping.

Arbitrary Assignment: The assignment of items to categories in nominal data is often arbitrary and based on some subjective or contextual criteria. For example, assigning items to categories like “red,” “blue,” or “green” for colors is arbitrary.

No Mathematical Operations: Arithmetic operations like addition, subtraction, or multiplication are not meaningful with nominal data because there is no numerical significance to the categories.

Examples of nominal data include:

• Gender categories (e.g., “male,” “female,” “other”).
• Marital status (e.g., “single,” “married,” “divorced,” “widowed”).
• Types of animals (e.g., “cat,” “dog,” “horse,” “bird”).
• Ethnicity or race (e.g., “Caucasian,” “African American,” “Asian,” “Hispanic”).

1.2. Ordinal Data:

Ordinal data is a type of categorical data that represents values with a meaningful order or ranking but does not have a consistent or evenly spaced numerical difference between the values. In other words, ordinal data has categories that can be ordered or ranked, but the intervals between the categories are not uniform or measurable.

Non-Numeric Labels: The categories in ordinal data are typically represented by non-numeric labels or symbols, such as “low,” “medium,” and “high” for levels of satisfaction or “small,” “medium,” and “large” for T-shirt sizes.

No Fixed Intervals: Unlike interval or ratio data, where the intervals between values have a consistent meaning and can be measured, ordinal data does not have fixed or uniform intervals. In other words, you cannot say that the difference between “low” and “medium” is the same as the difference between “medium” and “high.”

Limited Arithmetic Operations: Arithmetic operations like addition and subtraction are not meaningful with ordinal data because the intervals between categories are not quantifiable. However, some basic operations like counting frequencies, calculating medians, or finding modes can still be performed.

Examples of ordinal data include:

• Educational attainment levels (e.g., “high school,” “bachelor’s degree,” “master’s degree”).
• Customer satisfaction ratings (e.g., “very dissatisfied,” “somewhat dissatisfied,” “neutral,” “satisfied,” “very satisfied”).
• Likert scale responses (e.g., “strongly disagree,” “disagree,” “neutral,” “agree,” “strongly agree”).

2. Quantitative Data (Numerical Data)

Quantitative data represents quantities and can be measured.

2.1. Discrete Data:

Discrete data refers to a type of data that consists of distinct, separate values or categories. These values are typically counted and are often whole numbers, although they don’t have to be limited to integers. Discrete data can only take on specific, finite values within a defined range.

Key characteristics of discrete data include:

a. Countable Values : Discrete data represents individual, separate items or categories that can be counted or enumerated. For example, the number of students in a classroom, the number of cars in a parking lot, or the number of pets in a household are all discrete data.

b. Distinct Categories : Each value in discrete data represents a distinct category or class. These categories are often non-overlapping, meaning that an item can belong to one category only, with no intermediate values.

c. Gaps between Values : There are gaps or spaces between the values in discrete data. For example, if you are counting the number of people in a household, you can have values like 1, 2, 3, and so on, but you can’t have values like 1.5 or 2.75.

d. Often Represented Graphically with Bar Charts : Discrete data is commonly visualized using bar charts or histograms, where each category is represented by a separate bar, and the height of the bar corresponds to the frequency or count of that category.

* Examples of discrete data include:

The number of children in a family. The number of defects in a batch of products. The number of goals scored by a soccer team in a season. The number of days in a week (Monday, Tuesday, etc.). The types of cars in a parking lot (sedan, SUV, truck).

2.2. Continuous Data:

Continuous data, also known as continuous variables or quantitative data, is a type of data that can take on an infinite number of values within a given range. It represents measurements that can be expressed with a high level of precision and are typically numeric in nature. Unlike discrete data, which consists of distinct, separate values, continuous data can have values at any point along a continuous scale.

Precision: Continuous data is often associated with high precision, meaning that measurements can be made with great detail. For example, temperature, height, and weight can be measured to multiple decimal places.

No Gaps or Discontinuities: There are no gaps, spaces, or jumps between values in continuous data. You can have values that are very close to each other without any distinct categories or separations.

Graphical Representation: Continuous data is commonly visualized using line charts or scatter plots, where data points are connected with lines to show the continuous nature of the data.

Examples of continuous data include:

• Temperature readings, such as 20.5°C or 72.3°F.
• Height measurements, like 175.2 cm or 5.8 feet.
• Weight measurements, such as 68.7 kg or 151.3 pounds.
• Time intervals, like 3.45 seconds or 1.25 hours.
• Age of individuals, which can include decimals (e.g., 27.5 years).

3. Time-Series Data:

Time-series data is a type of data that is collected or recorded over a sequence of equally spaced time intervals. It represents how a particular variable or set of variables changes over time. Each data point in a time series is associated with a specific timestamp, which can be regular (e.g., hourly, daily, monthly) or irregular (e.g., timestamps recorded at random intervals).

Equally Spaced or Irregular Intervals: Time series can have equally spaced intervals, such as daily stock prices, or irregular intervals, like timestamped customer orders. The choice of interval depends on the nature of the data and the context of the analysis.

Seasonality and Trends: Time-series data often exhibits seasonality, which refers to repeating patterns or cycles, and trends, which represent long-term changes or movements in the data. Understanding these patterns is crucial for forecasting and decision-making.

Noise and Variability: Time series may contain noise or random fluctuations that make it challenging to discern underlying patterns. Statistical techniques are often used to filter out noise and identify meaningful patterns.

Applications: Time-series data is widely used in various fields, including finance (stock prices, economic indicators), meteorology (weather data), epidemiology (disease outbreaks), and manufacturing (production processes), among others. It is valuable for making predictions, monitoring trends, and understanding the dynamics of processes over time.

Visualization : Line charts are most suitable for time-series data.

4. Conclusion

Understanding the types of data is crucial as each type requires different methods of analysis. For instance, you wouldn’t use the same statistical test for nominal data as you would for continuous data. By categorizing your data correctly, you can apply the most suitable statistical tools and draw accurate conclusions.

More Articles

Correlation – connecting the dots, the role of correlation in data analysis, hypothesis testing – a deep dive into hypothesis testing, the backbone of statistical inference, sampling and sampling distributions – a comprehensive guide on sampling and sampling distributions, law of large numbers – a deep dive into the world of statistics, central limit theorem – a deep dive into central limit theorem and its significance in statistics, skewness and kurtosis – peaks and tails, understanding data through skewness and kurtosis”, similar articles, complete introduction to linear regression in r, how to implement common statistical significance tests and find the p value, logistic regression – a complete tutorial with examples in r.

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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1.3: Presentation of Data

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Learning Objectives

• To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example \(\PageIndex{1}\)

The data obtained by measuring the age of \(21\) randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array} \nonumber \]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \nonumber \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value \(x\) is listed in the first row and its frequency \(f\), which is the number of times the value \(x\) appears in the data set, is listed below it in the second row.

Example \(\PageIndex{2}\)

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array} \nonumber \]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

• Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

Data Presentation

Josée Dupuis, PhD, Professor of Biostatistics, Boston University School of Public Health

Wayne LaMorte, MD, PhD, MPH, Professor of Epidemiology, Boston University School of Public Health

Introduction

While graphical summaries of data can certainly be powerful ways of communicating results clearly and unambiguously in a way that facilitates our ability to think about the information, poorly designed graphical displays can be ambiguous, confusing, and downright misleading. The keys to excellence in graphical design and communication are much like the keys to good writing. Adhere to fundamental principles of style and communicate as logically, accurately, and clearly as possible. Excellence in writing is generally achieved by avoiding unnecessary words and paragraphs; it is efficient. In a similar fashion, excellence in graphical presentation is generally achieved by efficient designs that avoid unnecessary ink.

Excellence in graphical presentation depends on:

• Choosing the best medium for presenting the information
• Designing the components of the graph in a way that communicates the information as clearly and accurately as possible.

Table or Graph?

• Tables are generally best if you want to be able to look up specific information or if the values must be reported precisely.
• Graphics are best for illustrating trends and making comparisons

The side by side illustrations below show the same information, first in table form and then in graphical form. While the information in the table is precise, the real goal is to compare a series of clinical outcomes in subjects taking either a drug or a placebo. The graphical presentation on the right makes it possible to quickly see that for each of the outcomes evaluated, the drug produced relief in a great proportion of subjects. Moreover, the viewer gets a clear sense of the magnitude of improvement, and the error bars provided a sense of the uncertainty in the data.

Principles for Table Display

• Sort table rows in a meaningful way
• Avoid alphabetical listing!
• Use rates, proportions or ratios in addition (or instead of) totals
• Show more than two time points if available
• Multiple time points may be better presented in a Figure
• Similar data should go down columns
• Highlight important comparisons
• Show the source of the data

Consider the data in the table below from http://www.cancer.gov/cancertopics/types/commoncancers

Our ability to quickly understand the relative frequency of these cancers is hampered by presenting them in alphabetical order. It is much easier for the reader to grasp the relative frequency by listing them from most frequent to least frequent as in the next table.

However, the same information might be presented more effectively with a dot plot, as shown below.

Data from http://www.cancer.gov/cancertopics/types/commoncancers

Principles of Graphical Excellence from E.R. Tufte

Pattern perception.

Pattern perception is done by

• Detection: recognition of geometry encoding physical values
• Assembly: grouping of detected symbol elements; discerning overall patterns in data
• Estimation: assessment of relative magnitudes of two physical values

Geographic Variation in Cancer

As an example, Tufte offers a series of maps that summarize the age-adjusted mortality rates for various types of cancer in the 3,056 counties in the United States. The maps showing the geographic variation in stomach cancer are shown below.

These maps summarize an enormous amount of information and present it efficiently, coherently, and effectively.in a way that invites the viewer to make comparisons and to think about the substance of the findings. Consider, for example, that the region to the west of the Great Lakes was settled largely by immigrants from Germany and Scand anavia, where traditional methods of preserving food included pickling and curing of fish by smoking. Could these methods be associated with an increased risk of stomach cancer?

John Snow's Spot Map of Cholera Cases

Consider also the spot map that John Snow presented after the cholera outbreak in the Broad Street section of London in September 1854. Snow ascertained the place of residence or work of the victims and represented them on a map of the area using a small black disk to represent each victim and stacking them when more than one occurred at a particular location. Snow reasoned that cholera was probably caused by something that was ingested, because of the intense diarrhea and vomiting of the victims, and he noted that the vast majority of cholera deaths occurred in people who lived or worked in the immediate vicinity of the broad street pump (shown with a red dot that we added for clarity). He further ascertained that most of the victims drank water from the Broad Street pump, and it was this evidence that persuaded the authorities to remove the handle from the pump in order to prevent more deaths.

Humans can readily perceive differences like this when presented effectively as in the two previous examples. However, humans are not good at estimating differences without directly seeing them (especially for steep curves), and we are particularly bad at perceiving relative angles (the principal perception task used in a pie chart).

The use of pie charts is generally discouraged. Consider the pie chart on the left below. It is difficult to accurately assess the relative size of the components in the pie chart, because the human eye has difficulty judging angles. The dot plot on the right shows the same data, but it is much easier to quickly assess the relative size of the components and how they changed from Fiscal Year 2000 to Fiscal Year 2007.

Consider the information in the two pie charts below (showing the same information).The 3-dimensional pie chart on the left distorts the relative proportions. In contrast the 2-dimensional pie chart on the right makes it much easier to compare the relative size of the varies components..

More Principles of Graphical Excellence

Exclude unneeded dimensions.

These 3-dimensional techniques distort the data and actually interfere with our ability to make accurate comparisons. The distortion caused by 3-dimensional elements can be particularly severe when the graphic is slanted at an angle or when the viewer tends to compare ends up unwittingly comparing the areas of the ink rather than the heights of the bars.

It is much easier to make comparisons with a chart like the one below.

Source: Huang, C, Guo C, Nichols C, Chen S, Martorell R. Elevated levels of protein in urine in adulthood after exposure to

the Chinese famine of 1959–61 during gestation and the early postnatal period. Int. J. Epidemiol. (2014) 43 (6): 1806-1814 .

Omit "Chart Junk"

Consider these two examples.

Here is a simple enumeration of the number of pets in a neighborhood. There is absolutely no reason to connect these counts with lines. This is, in fact, confusing and inappropriate and nothing more than "chart junk."

Source: http://www.go-education.com/free-graph-maker.html

Moiré Vibration

Moiré effects are sometimes used in modern art to produce the appearance of vibration and movement. However, when these effects are applied to statistical presentations, they are distracting and add clutter because the visual noise interferes with the interpretation of the data.

Tufte presents the example shown below from Instituto de Expansao Commercial, Brasil, Graphicos Estatisticas (Rio de Janeiro, 1929, p. 15).

 While the intention is to present quantitative information about the textile industry, the moiré effects do not add anything, and they are distracting, if not visually annoying.

Present Data to Facilitate Comparisons

Here is an attempt to compare catches of cod fish and crab across regions and to relate the variation to changes in water temperature. The problem here is that the Y-axes are vastly different, making it hard to sort out what's really going on. Even the Y-axes for temperature are vastly different.

http://seananderson.ca/courses/11-multipanel/multipanel.pdf1

The ability to make comparisons is greatly facilitated by using the same scales for axes, as illustrated below.

Data source: Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease:

the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279-81. PMID: 14819398

It is also important to avoid distorting the X-axis. Note in the example below that the space between 0.05 to 0.1 is the same as space between 0.1 and 0.2.

Source: Park JH, Gail MH, Weinberg CR, et al. Distribution of allele frequencies and effect sizes and

their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A. 2011; 108:18026-31.

Consider the range of the Y-axis. In the examples below there is no relevant information below $40,000, so it is not necessary to begin the Y-axis at 0. The graph on the right makes more sense.

Also, consider using a log scale. this can be particularly useful when presenting ratios as in the example below.

Source: Broman KW, Murray JC, Sheffield VC, White RL, Weber JL (1998) Comprehensive human genetic maps:

Individual and sex-specific variation in recombination. American Journal of Human Genetics 63:861-869, Figure 1

We noted earlier that pie charts make it difficult to see differences within a single pie chart, but this is particularly difficult when data is presented with multiple pie charts, as in the example below.

Source: Bell ML, et al. (2007) Spatial and temporal variation in PM2.5 chemical composition in the United States

for health effects studies. Environmental Health Perspectives 115:989-995, Figure 3

When multiple comparisons are being made, it is essential to use colors and symbols in a consistent way, as in this example.

Source: Manning AK, LaValley M, Liu CT, et al.  Meta-Analysis of Gene-Environment Interaction:

Joint Estimation of SNP and SNP x Environment Regression Coefficients.  Genet Epidemiol 2011, 35(1):11-8.

Avoid putting too many lines on the same chart. In the example below, the only thing that is readily apparent is that 1980 was a very hot summer.

Data from National Weather Service Weather Forecast Office at

http://www.srh.noaa.gov/tsa/?n=climo_tulyeartemp

Make Efficient Use of Space

Reduce the ratio of ink to information.

This isn't efficient, because this graphic is totally uninformative.

Source: Mykland P, Tierney L, Yu B (1995) Regeneration in Markov chain samplers.  Journal of the American Statistical Association 90:233-241, Figure 1

Bar graphs add ink without conveying any additional information, and they are distracting. The graph below on the left inappropriately uses bars which clutter the graph without adding anything. The graph on the right displays the same data, by does so more clearly and with less clutter.

Multiple Types of Information on the Same Figure

Choosing the best graph type, bar charts, error bars and dot plots.

As noted previously, bar charts can be problematic. Here is another one presenting means and error bars, but the error bars are misleading because they only extend in one direction. A better alternative would have been to to use full error bars with a scatter plot, as illustrated previously (right).

Consider the four graphs below presenting the incidence of cancer by type. The upper left graph unnecessary uses bars, which take up a lot of ink. This layout also ends up making the fonts for the types of cancer too small. Small font is also a problem for the dot plot at the upper right, and this one also has unnecessary grid lines across the entire width.

The graph at the lower left has more readable labels and uses a simple dot plot, but the rank order is difficult to figure out.

The graph at the lower right is clearly the best, since the labels are readable, the magnitude of incidence is shown clearly by the dot plots, and the cancers are sorted by frequency.

Single Continuous Numeric Variable

In this situation a cumulative distribution function conveys the most information and requires no grouping of the variable. A box plot will show selected quantiles effectively, and box plots are especially useful when stratifying by multiple categories of another variable.

Histograms are also possible. Consider the examples below.

Two Variables

 The two graphs below summarize BMI (Body Mass Index) measurements in four categories, i.e., younger and older men and women. The graph on the left shows the means and 95% confidence interval for the mean in each of the four groups. This is easy to interpret, but the viewer cannot see that the data is actually quite skewed. The graph on the right shows the same information presented as a box plot. With this presentation method one gets a better understanding of the skewed distribution and how the groups compare.

The next example is a scatter plot with a superimposed smoothed line of prediction. The shaded region embracing the blue line is a representation of the 95% confidence limits for the estimated prediction. This was created using "ggplot" in the R programming language.

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf (page 121)

Multivariate Data

The example below shows the use of multiple panels.

Source: Cleveland S. The Elements of Graphing Data. Hobart Press, Summit, NJ, 1994.

Displaying Uncertainty

• Error bars showing confidence limits
• Confidence bands drawn using two lines
• Shaded confidence bands
• Bayesian credible intervals
• Bayesian posterior densities

Confidence Limits

Shaded Confidence Bands

Source: Frank E. Harrell Jr. on graphics:  http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf

Source: Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Forest Plot

This is a Forest plot summarizing 26 studies of cigarette smoke exposure on risk of lung cancer. The sizes of the black boxes indicating the estimated odds ratio are proportional to the sample size in each study.

Data from Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Summary Recommendations

• In general, avoid bar plots
• Avoid chart junk and the use of too much ink relative to the information you are displaying. Keep it simple and clear.
• Avoid pie charts, because humans have difficulty perceiving relative angles.
• Pay attention to scale, and make scales consistent.
• Explore several ways to display the data!

12 Tips on How to Display Data Badly

Adapted from Wainer H.  How to Display Data Badly.  The American Statistician 1984; 38: 137-147. 

• Show as few data as possible
• Hide what data you do show; minimize the data-ink ratio
• Ignore the visual metaphor altogether
• Only order matters
• Graph data out of context
• Change scales in mid-axis
• Emphasize the trivial;  ignore the important
• Jiggle the baseline
• Alphabetize everything.
• Make your labels illegible, incomplete, incorrect, and ambiguous.
• More is murkier: use a lot of decimal places and make your graphs three dimensional whenever possible.
• If it has been done well in the past, think of another way to do it

Additional Resources

• Stephen Few: Designing Effective Tables and Graphs. http://www.perceptualedge.com/images/Effective_Chart_Design.pdf
• Gary Klaas: Presenting Data: Tabular and graphic display of social indicators. Illinois State University, 2002. http://lilt.ilstu.edu/gmklass/pos138/datadisplay/sections/goodcharts.htm (Note: The web site will be discontinued to be replaced by the Just Plain Data Analysis site).

Industrial Design of Experiments pp 1–60 Cite as

Data Presentations, Statistical Distributions, Quality Tools, and Relationship to DoE

• Sammy Shina 2  
• First Online: 03 January 2022

1123 Accesses

In this introductory chapter, principles of basic statistics, including data presentation techniques for visual and qualitative analysis are shown with examples and guidelines for use in problem solving, quality improvements and sequential analysis. Probability Distributions and their use in quantitative quality tools such as DoE, Control charts and Six Sigma and their relationships to each other are demonstrated with formulas and examples as well as their respective role in achieving quality and reliability of industrial products.

The origins and concepts of Six Sigma are explained, with emphasis on new product design and its relationship to manufacturing quality performance. Process capability as input to Six sigma is discussed, including two common versions (C p and C pk ). Different types of Control Charts are presented with their use with variable or attribute data and constant or variable sample sizes with examples and analysis. A hierarchical approach of using quality tools including TQM, DMAIC, Six Sigma, Control Charts and DoE to achieve world class quality is demonstrated. The proper sequence of using these tools is shown, depending on whether optimizing current or new processes and designs.

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Shina, S., Green Design and Manufacturing , McGraw Hill professional series, April 2008

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Additional Reading Material

Advanced Product Quality Planning and Control Plan (APQP) , Southfield, MI: Automotive Industries Action Group (AIAG), 1995.

Bajaria, H., “Six Sigma-Moving Beyond the Hype,” Annual Quality Congress Proceedings, ASQ, 1999.

Box, G., J. Hunter and Hunter, W., Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition, New York: Wiley-Interscience, 2005.

Harry, M., The Nature of Six Sigma Quality , Rolling Meadows, IL: Motorola University, 1988.

Harry, M. and Schroeder R., Six Sigma , New York: Doubleday, 2000.

Hahn, G. et al., “The Evolution of Six Sigma,” Quality Engineering , vol. 12, no. 3, pp. 317–326, 2000.

Iversen, W., “The Six Sigma Shootout,” Assembly Magazine , June 1993, pp. 20-24.

Kemtovicz, R., New Product Development: Design and Analysis , New York: Wiley, 1992.

Liker, J., The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer , 1 st Edition. New York: McGraw-Hill, 2003.

Shina, S., Engineering Project Management , McGraw Hill professional series, December 2014

Shina S., “A Cpk-Based Toolkit for Tolerance Analysis and Design,” Engineering Design Conference, UCL, London; July 2002.

Shina, S. and Saigal A. “Manufacturing Costs for electronic Products”, Volume 3 of the Encyclopedia of Materials, Elesevier Press, November 2001, pp 2727-2735.

Shina, S. and Saigal A., “Using Cpk as a Design Tool for New System Development”, Journal of Quality Engineering, Volume XII, Number 4, 2000, pp. 333-349

Shina S. and Saigal A., “Using Cpk as Design Tool for New System Development,” International Conference on Engineering Design (ICED), Vol. 1, pp. 357-360, Munich, August 1999.

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Shina, S. (2022). Data Presentations, Statistical Distributions, Quality Tools, and Relationship to DoE. In: Industrial Design of Experiments. Springer, Cham. https://doi.org/10.1007/978-3-030-86267-1_1

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2024

Leah Nguyen • 05 Apr 2024 • 11 min read

There are different ways of presenting data, so which one is suited you the most? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

• What are Methods of Data Presentations?
• #1 – Tabular

#2 – Text

#3 – pie chart, #4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

• 5 Mistakes to Avoid
• Best Method of Data Presentation

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day. Let’s dive into 10 ways to present data efficiently.

#1 – Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of presentation of data. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

Recordings to ways of displaying data, we shouldn’t overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quiz and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should set your session with open-ended questions , to avoid dead-communication!

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors’ behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

What is chart presentation?

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should use charts for presentation?

You should use charts to ensure your contents and visual look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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• v.23(Suppl 2); 2019 Jun

An Introduction to Statistics – Data Types, Distributions and Summarizing Data

Priya ranganathan.

1 Department of Anesthesiology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India

Nithya J Gogtay

2 Department of Clinical Pharmacology, King Edward Memorial Hospital, Mumbai, Maharashtra, India

The purpose of research is to gather data, which can then be used to inform decision-making. Data can be of various types and an understanding of this is crucial for its proper analysis and interpretation. In this article, we look at various types and distributions of data, and methods to summarize this data.

How to cite this article

Ranganathan P, Gogtay NJ, An Introduction to Statistics – Data Types, Distributions and Summarizing Data. Indian J Crit Care Med 2019;23(Suppl 2):S169–S170.

INTRODUCTION

In the first article of this series, we look at types of data and the methods used to describe or summarize data. Data is defined as ‘factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation’. 1 As statistics begins with data collection, understanding data is important, as it will help apply the right statistical tests, make the appropriate assumptions and draw meaningful and robust conclusions.

Classification of Data

At the highest level, data can be broadly classified as qualitative data (also known as categorical data ) or quantitative data (also known as numerical data ).

Qualitative or categorical data answers the question ‘What type’ or ‘Which category’. Gender (male/female), marital status (married/divorced/widowed/single), severity of pain on visual analog scale (mild/moderate/severe) are examples of qualitative or categorical data. Within categorical data, there are subtypes. Nominal data is data that does not follow any particular natural order, e.g. marital status (married / divorced/ widowed/ single). Here, there is no sequence between the various categories. In contrast, severity of pain can be ranked in ascending order as none/mild/moderate/severe/unbearable. This is known as ordinal data . Categorical data can also be classified depending on the number of categories. If only two categories are present e.g., dead/alive, married/unmarried – then the data is binary or dichotomous . If more than two categories are present e.g., married/divorced/widowed/single then this is non-binary data . In summary, qualitative data deals with characteristics, traits or descriptors or judgements.

Note: While collecting data, it is useful to collect it in as many categories as required so that information is not missed out. Subsequently, if needed, the researcher can rearrange the categories and collapse them into binary data or two categories for ease of analysis and interpretation.

Quantitative or numerical data is data that can be counted and answers the question ‘How many’ or ‘How much”. For example, age, height, weight, blood pressure, number of children, number of hospital visits. Numerical data can be further classified into discrete data and continuous data. Discrete data is data that can only be counted in whole numbers. e.g., number of hospital visits, Glasgow Coma Score, Richmond Agitation Sedation Scale, modified Rankin scale. This type of data cannot be represented in decimals – so Glasgow Coma Scale score can be 7 or 8 not 7.5. Continuous data is data that theoretically has no gap between data points can be counted in decimals (depending on the precision of the measuring instrument) e.g., blood glucose, hemoglobin, serum lipids. In summary, quantitative data deals with objective measurements.

Note: The same outcome can be measured as either numerical or categorical data depending on the measuring tool. For example, pain measured on a visual analog scale is categorical data (none/mild/moderate/severe/unbearable). The same outcome can be measured on a numerical rating scale of 0–10 where 0 represents no pain and 10 represents unbearable pain. Thus, the purpose of data is to finally answer the research question and the classification of data is not sacrosanct.

Distribution of Data

The distribution of data obtained from a sample is crucial to understand how to analyze it. There are various probability distributions for different types of data. Of these, one particularly important type used in everyday research is the distribution of continuous numerical data. This data may either be normally distributed or skewed.

Normally distributed data is data that follows a symmetric bell-shaped curve (Gaussian distribution) ( Fig. 1 ). Here, most of the data observations are centered around the middle and the curve gradually tapers on either side. Statisticians have formal methods of assessing normality of distribution. The simplest of these is to plot a histogram and visually inspect the distribution of the data. Other methods involve testing normality using a Q-Q plot or using the Kolmogorov Smirnov or the Shapiro Wilk tests.

Normal distribution of data - the mean, median and mode are fairly close to each other

As against this, skewed data does not have a symmetric distribution and may have a long tail on one side ( Fig. 2 ). Duration of surgery, duration of hospital stay or ICU stay are examples of data that is likely to be skewed.

Skewed distribution of data - the mean, median and mode differ from each other

Note: Distribution of data is important in the choice of statistical test for analysis. Tests that assume a normal or Gaussian distribution are known as parametric tests and tests that do not make assumptions about distribution are known as non-parametric or distribution-free tests. The former are in general more powerful tests.

Summarizing Data

It is often difficult to represent individual data from multiple participants in a study unless these numbers are really small. For example, if there are 100 participants in a study, we cannot enumerate the ages of each of the participants. We therefore use certain measures to summarize this data and allow us to represent it easily.

For categorical data, we use percentages and depict this using bar graphs or pie charts. Refer to Table 1 of the article by Nielsen and colleagues, in which baseline characteristics of participants are listed. 2 Gender, medical history and some of the characteristics of the cardiac arrest are examples of categorical data. These are shown as actual numbers with percentages in brackets.

For numerical data, we depict the ‘average’, which is “middle” of the data and the ‘dispersion’, which tells us how far away from the ‘average’ most of the values lie. If data is normally distributed, the average is the arithmetic mean . The spread of the data around the mean is depicted by the standard deviation . The larger the standard deviation, the more dispersed is the data (i.e., higher is the variance within the data). For example, in Table 1 of the article by Nielsen, age, body temperature, serum pH and serum lactate are all represented by the mean and standard deviation. 2 Similarly, in Table 1 of the article by Nishikimi, several baseline characteristics are represented by their mean and standard deviation. 3 For normally distributed data, the mean, median and mode are fairly close to each other.

For skewed data, the arithmetical mean does not give an idea of the true average. We therefore use another summary statistic called the median (or the 50th centile). For this, the data is arranged in ascending or descending order and divided into 100 equal parts (percentiles). The point that corresponds to the 50th percentile is the median (half of the observations are greater than and half are lesser than this value). The 25th to 75th percentiles give the inter-quartile range . In Table 1 of the article by Nielsen, time from cardiac arrest to event and Glasgow Coma scale are both examples of data that is represented by median and interquartile range. 2 Graphically, a box-and-whisker plot or a stem-and-leaf plot help good visualization of skewed data. Readers can refer to Table 2A of the article by Nishikimi, in which duration of ICU stay is depicted by a box and whiskers plot. 3 Unlike the mean, the median is not affected by extreme data points.

Rarely, one will find reference to another measure called the mode . The mode represents the observation that has the highest frequency. For example, if one was looking at age-wise distribution of incidence of a particular cancer, then the mode would tell us which age group had the highest incidence.

Care with Data

The science of statistics begins with collection of data. Apart from understanding data types, distributions and how to summarize data, we need to be meticulous in the process of collection and extremely careful in writing, entering data into software or excel sheets or transcribing data, as errors can seriously impact conclusions and the consequent decision making in patient care.

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• Presentation of Data

Data Presenting for Clearer Reference

Imagine the statistical data without a definite presentation, will be burdensome! Data presentation is one of the important aspects of Statistics. Presenting the data helps the users to study and explain the statistics thoroughly. We are going to discuss this presentation of data and know-how information is laid down methodically. 

In this context, we are going to present the topic - Presentation of Data which is to be referred to by the students and the same is to be studied in regard to the types of presentations of data. 

Presentation of Data and Information

Statistics is all about data. Presenting data effectively and efficiently is an art. You may have uncovered many truths that are complex and need long explanations while writing. This is where the importance of the presentation of data comes in. You have to present your findings in such a way that the readers can go through them quickly and understand each and every point that you wanted to showcase. As time progressed and new and complex research started happening, people realized the importance of the presentation of data to make sense of the findings.

Define Data Presentation

Data presentation is defined as the process of using various graphical formats to visually represent the relationship between two or more data sets so that an informed decision can be made based on them.

Types of Data Presentation

Broadly speaking, there are three methods of data presentation:

Diagrammatic

Textual Ways of Presenting Data

Out of the different methods of data presentation, this is the simplest one. You just write your findings in a coherent manner and your job is done. The demerit of this method is that one has to read the whole text to get a clear picture. Yes, the introduction, summary, and conclusion can help condense the information.

Tabular Ways of Data Presentation and Analysis

To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year, sex, age, and other things like these). It is against these attributes that data is written within a cell.

Diagrammatic Presentation: Graphical Presentation of Data in Statistics

This kind of data presentation and analysis method says a lot with dramatically short amounts of time.

Diagrammatic Presentation has been divided into further categories:

Geometric Diagram

When a Diagrammatic presentation involves shapes like a bar or circle, we call that a Geometric Diagram. Examples of Geometric Diagram

Bar Diagram

Simple Bar Diagram

Simple Bar Diagram is composed of rectangular bars. All of these bars have the same width and are placed at an equal distance from each other. The bars are placed on the X-axis. The height or length of the bars is used as the means of measurement. So, on the Y-axis, you have the measurement relevant to the data. 

Suppose, you want to present the run scored by each batsman in a game in the form of a bar chart. Mark the runs on the Y-axis - in ascending order from the bottom. So, the lowest scorer will be represented in the form of the smallest bar and the highest scorer in the form of the longest bar.

Multiple Bar Diagram

(Image will be uploaded soon)

In many states of India, electric bills have bar diagrams showing the consumption in the last 5 months. Along with these bars, they also have bars that show the consumption that happened in the same months of the previous year. This kind of Bar Diagram is called Multiple Bar Diagrams.

Component Bar Diagram

(image will be uploaded soon)

Sometimes, a bar is divided into two or more parts. For example, if there is a Bar Diagram, the bars of which show the percentage of male voters who voted and who didn’t and the female voters who voted and who didn’t. Instead of creating separate bars for who did and who did not, you can divide one bar into who did and who did not.

A pie chart is a chart where you divide a pie (a circle) into different parts based on the data. Each of the data is first transformed into a percentage and then that percentage figure is multiplied by 3.6 degrees. The result that you get is the angular degree of that corresponding data to be drawn in the pie chart. So, for example, you get 30 degrees as the result, on the pie chart you draw that angle from the center.

Frequency Diagram

Suppose you want to present data that shows how many students have 1 to 2 pens, how many have 3 to 5 pens, how many have 6 to 10 pens (grouped frequency) you do that with the help of a Frequency Diagram. A Frequency Diagram can be of many kinds:

Where the grouped frequency of pens (from the above example) is written on the X-axis and the numbers of students are marked on the Y-axis. The data is presented in the form of bars.

Frequency Polygon

When you join the midpoints of the upper side of the rectangles in a histogram, you get a Frequency Polygon

Frequency Curve

When you draw a freehand line that passes through the points of the Frequency Polygon, you get a Frequency Curve.

Ogive 

Suppose 2 students got 0-20 marks in maths, 5 students got 20-30 marks and 4 students got 30-50 marks in Maths. So how many students got less than 50 marks? Yes, 5+2=7. And how many students got more than 20 marks? 5+4=9. This type of more than and less than data are represented in the form of the ogive. The meeting point of the less than and more than line will give you the Median.

Arithmetic Line Graph

If you want to see the trend of Corona infection vs the number of recoveries from January 2020 to December 2020, you can do that in the form of an Arithmetic Line Graph. The months should be marked on the X-axis and the number of infections and recoveries are marked on the Y-axis. You can compare if the recovery is greater than the infection and if the recovery and infection are going at the same rate or not with the help of this Diagram.

Did You Know?

Sir Ronald Aylmer Fisher is known as the father of modern statistics.

FAQs on Presentation of Data

1. What are the 4 types of Tabular Presentation?

The tabular presentation method can be further divided into 4 categories:

Qualitative

Quantitative

Qualitative classification is done when the attributes in the table are some kind of ‘quality’ or feature. Suppose you want to make a table where you would show how many batsmen made half-centuries and how many batsmen made centuries in IPL 2020. Notice that the data would have only numbers - no age, sex, height is needed. This type of tabulation is called quantitative tabulation.

If you want to make a table that would inform which year’s world cup, which team won. The classifying variable, here, is year or time. This kind of classification is called Temporal classification.

If you want to list the top 5 coldest places in the world. The classifying variable here would be a place in each case. This kind of classification is called Spatial Classification.

2. Are bar charts and histograms the Same?

No, they are not the same. With a histogram, you measure the frequency of quantitative data. With bar charts, you compare categorical data.

3. What is the definition of Data Presentation?

When research work is completed, the data gathered from it can be quite large and complex. Organizing the data in a coherent, easy-to-understand, quick to read and graphical way is called data presentation.

Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

6 Types of Data in Statistics & Research: Key in Data Science

Understanding the different types of data (in statistics, marketing research, or data science) allows you to pick the data type that most closely matches your needs and goals.

Whether you are a businessman, marketer, data scientist, or another professional who works with some kinds of data, you should be familiar with the key list of data types.

Why? Because the various data classifications allow you to correctly use measurements and thus to correctly make decisions.

On this page:

• The most common data types (with examples) in statistics, research, and data science. Simply explained.
• Infographics in PDF

Qualitative vs Quantitative Data

1. Quantitative data

Quantitative data seems to be the easiest to explain. It answers key questions such as “how many, “how much” and “how often”.

Quantitative data can be expressed as a number or can be quantified. Simply put, it can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented by a wide variety of statistical types of graphs and charts such as line, bar graph, scatter plot, and etc.

Examples of quantitative data:

• Scores on tests and exams e.g. 85, 67, 90 and etc.
• The weight of a person or a subject.
• Your shoe size.
• The temperature in a room.

There are 2 general types of quantitative data: discrete data and continuous data. We will explain them later in this article.

2. Qualitative data

Qualitative data can’t be expressed as a number and can’t be measured. Qualitative data consist of words, pictures, and symbols, not numbers.

Qualitative data is also called categorical data  because the information can be sorted by category, not by number.

Qualitative data can answer questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

• Colors e.g. the color of the sea
• Your favorite holiday destination such as Hawaii, New Zealand and etc.
• Names as John, Patricia,…..
• Ethnicity such as American Indian, Asian, etc.

More you can see on our post qualitative vs quantitative data .

There are 2 general types of qualitative data: nominal data and ordinal data. We will explain them after a while.

Download the following infographic in PDF

Nominal vs Ordinal Data

3. Nominal data

Nominal data is used just for labeling variables, without any type of quantitative value. The name ‘nominal’ comes from the Latin word “nomen” which means ‘name’.

The nominal data just name a thing without applying it to order. Actually, the nominal data could just be called “labels.”

Examples of Nominal Data:

• Gender (Women, Men)
• Hair color (Blonde, Brown, Brunette, Red, etc.)
• Marital status (Married, Single, Widowed)
• Ethnicity (Hispanic, Asian)

As you see from the examples there is no intrinsic ordering to the variables.

Eye color is a nominal variable having a few categories (Blue, Green, Brown) and there is no way to order these categories from highest to lowest.

4. Ordinal data

Ordinal data shows where a number is in order. This is the crucial difference from nominal types of data.

Ordinal data is data which is placed into some kind of order by their position on a scale. Ordinal data may indicate superiority.

However, you cannot do arithmetic with ordinal numbers because they only show sequence.

Ordinal variables are considered as “in between” qualitative and quantitative variables.

In other words, the ordinal data is qualitative data for which the values are ordered.

In comparison with nominal data, the second one is qualitative data for which the values cannot be placed in an ordered.

We can also assign numbers to ordinal data to show their relative position. But we cannot do math with those numbers. For example: “first, second, third…etc.”

Examples of Ordinal Data:

• The first, second and third person in a competition.
• Letter grades: A, B, C, and etc.
• When a company asks a customer to rate the sales experience on a scale of 1-10.
• Economic status: low, medium and high.

Much more on the topic plus a quiz, you can learn in our post: nominal vs ordinal data .

Discrete vs Continuous Data

As we mentioned above discrete and continuous data are the two key types of quantitative data.

In statistics, marketing research, and data science, many decisions depend on whether the basic data is discrete or continuous.

5. Discrete data

Discrete data is a count that involves only integers. The discrete values cannot be subdivided into parts.

For example, the number of children in a class is discrete data. You can count whole individuals. You can’t count 1.5 kids.

To put in other words, discrete data can take only certain values. The data variables cannot be divided into smaller parts.

It has a limited number of possible values e.g. days of the month.

Examples of discrete data:

• The number of students in a class.
• The number of workers in a company.
• The number of home runs in a baseball game.
• The number of test questions you answered correctly

6. Continuous data

Continuous data is information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value.

For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc.

You can record continuous data at so many different measurements – width, temperature, time, and etc. This is where the key difference from discrete types of data lies.

The continuous variables can take any value between two numbers. For example, between 50 and 72 inches, there are literally millions of possible heights: 52.04762 inches, 69.948376 inches and etc.

A good great rule for defining if a data is continuous or discrete is that if the point of measurement can be reduced in half and still make sense, the data is continuous.

Examples of continuous data:

• The amount of time required to complete a project.
• The height of children.
• The square footage of a two-bedroom house.
• The speed of cars.

Much more on the topic you can see in our detailed post discrete vs continuous data : with a comparison chart.

All of the different types of data have a critical place in statistics, research, and data science.

Data types work great together to help organizations and businesses from all industries build successful data-driven decision-making process .

Working in the data management area and having a good range of data science skills involves a deep understanding of various types of data and when to apply them. If you’re looking to enhance your data analysis skills, taking the best data science courses online can provide you with a solid foundation in understanding these essential data types.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Data is a simple record or collection of different numbers, characters, images, and others that are processed to form Information. In statistics , we have different types of data that are used to represent various information. In statistics, we analyze the data to obtain any meaningful information and thus categorizing data into different types is very important. Data types in statistics help us to make an informed decision about what type of process is used to analyze the data.

Here, in this article, we will learn about types of data in statistics in detail, examples, and others in detail. Before learning about data let’s first learn about Data.

Table of Content

What is data, what are types of data in statistics.

• Qualitative Data
• Quantitative Data

Difference between Quantitative and Qualitative Data

Difference between discrete and continuous data.

Data is defined as the collection of numbers, characters, images, and others that can arranged in some manner to form meaningful information. In statistics, the data is mainly the collection of numbers that is first studied then analyzed and presented in some way that we can get some meaningful insight from that data.

For example, 12, 19, 17, and 15 this is data.

The data in statistics is classified into four categories:

• Nominal data
• Ordinal data
• Discrete data
• Continuous data

Data Types In Statistics

In statistics, there are four main types of data: nominal, ordinal, interval, and ratio. These types of data are used to describe the nature of the data being collected or analyzed, and they help determine the appropriate statistical tests to use. In this essay, we will explore each type of data in detail, providing examples along the way.

Qualitative Data (Categorical Data)

As the name suggest Qualitative Data tells the features of the data in the statistics. Qualitative Data is also called Categorical Data and its categories the data into various categories. Qualitative data includes data such as gender of people, their family name and others in sample of population data.

Qualitative data is further categorized into two categories that includes,

Nominal Data

Ordinal data.

Nominal data is a type of data that consists of categories or names that cannot be ordered or ranked. Nominal data is often used to categorize observations into groups, and the groups are not comparable. In other words, nominal data has no inherent order or ranking. Examples of nominal data include gender (Male or female), race (White, Black, Asian), religion (Hinuduism, Christianity, Islam, Judaism), and blood type (A, B, AB, O).

Nominal data can be represented using frequency tables and bar charts, which display the number or proportion of observations in each category. For example, a frequency table for gender might show the number of males and females in a sample of people.

Nominal data is analyzed using non-parametric tests, which do not make any assumptions about the underlying distribution of the data. Common non-parametric tests for nominal data include Chi-Squared Tests and Fisher’s Exact Tests. These tests are used to compare the frequency or proportion of observations in different categories.

Ordinal data is a type of data that consists of categories that can be ordered or ranked. However, the distance between categories is not necessarily equal. Ordinal data is often used to measure subjective attributes or opinions, where there is a natural order to the responses. Examples of ordinal data include education level (Elementary, Middle, High School, College), job position (Manager, Supervisor, Employee), etc.

Ordinal data can be represented using bar charts, line charts. These displays show the order or ranking of the categories, but they do not imply that the distances between categories are equal.

Ordinal data is analyzed using non-parametric tests, which make no assumptions about the underlying distribution of the data. Common non-parametric tests for ordinal data include the Wilcoxon Signed-Rank test and Mann-Whitney U test.

Quantitative Data (Numerical Data)

Quantitavive Data is the type of the data that represents the numerical value of the data. They are also called the Numerical Data. This data type is used to represent the height, weight, length and other things of the data. Quantitative data is further classified into two categories that are,

Discrete Data

Continuous data.

Discrite data type is a type of data in statistics that only uses Discrete Value or Single Values. These data types have values that can be easily counted as whole numbers. The example of the discreate data types are,

• Height of Students in a class
• Marks of the students in a class test
• Weight of different members of a family, etc.

Continuous data is the type of the quantitative data that represent the data in a continuous range. The variable in the data set can have any value between the range of the data set. Examples of the continuous data types are,

• Temperature Range
• Salary range of Workers in a Factory, etc.

Quantitaive and Qualitative data has huge differences and the basic differences between them are studied in the table added below,

Discrete data and continuous data both come under Quantitaive data and the differences between them is studied in the table added below,

Data Types in Statistics – FAQs

1. what is data and its types.

Data is a collection of raw facts and numbers that are processed to form any information and there are generally two types of data in statistics that are, Qualitative Data and Quantitative Data.

2. What are 4 Types of Data in Statistics?

The 4 types of data in statistics are, Nominal data Ordinal data Discrete data Continuous data

3. What are the Main Types of Data?

There are two main types of data in mathematics that are, Qualitative Data Quantitative Data

4. What is Discrete Data?

The data that has a particular value rather that the range of the value is called the discrete data.

5. What is Continuous Data?

The data that lies between the highest and the lowest value are called the continuous data. The range of the continuous data is the difference between the highest and the lowest data.

6. What is Primary Data?

Primary data in mathematics is defined as the data that is collected for the first time. It is pure data and no analysis is performed in this data.

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What is Data in Statistics & Types Of Data With Examples

Data forms the bedrock of analysis and decision-making in statistics. Knowing data and its various types is essential for conducting meaningful statistical studies.

This article explores data and types of data in statistics. By understanding these concepts, you will be better equipped to interpret and utilize data effectively in your analysis.

What is Data?

Data encompasses all the information, observations, or measurements you gather through various means, such as surveys, experiments, or observations. It can take different forms, including numbers, text, images, or even sensory inputs like temperature readings or sound waves.

In statistics, data serves as the starting point for analysis. It's what you examine, manipulate, and interpret to draw conclusions or make predictions about a particular phenomenon or population.

What is the Role of Data in Statistics?

Data plays an important role in understanding and drawing conclusions. It forms the foundation for analysis, providing the evidence needed to make informed decisions. Without data, your statistical studies lack the real-world information necessary to be meaningful. 

Exploration is driven forward by examining and interpreting collected data. Through this process, you uncover patterns, relationships, and trends, aiding in making sense of the world around you. Ultimately, data serves as the guiding light, illuminating the path to understanding complex events.

What are the Types of Data in Statistics?

Data types are crucial in statistics because different types require different statistical methods for analysis. For instance, analyzing continuous data requires fundamentally different techniques from analyzing categorical data. Using the wrong method for a particular data type can lead to erroneous conclusions. Therefore, understanding the types of data you're working with enables you to select the appropriate method of analysis, ensuring accurate and reliable results.

In statistical analysis, data is broadly categorized into;

Nominal Data

Ordinal data, discrete data, continuous data.

Each type has its own characteristics, examples, and applications, which are essential for understanding and interpreting statistical information effectively.

Qualitative Data 

Qualitative data, also known as categorical data, consist of categories or labels that represent qualitative characteristics. It simply categorizes individuals or items based on shared attributes.

There are two types of qualitative data:

Nominal data are categories without any inherent order. Examples include gender (male, female), types of fruits (apple, banana, orange), and city names (New York, London, Paris). Nominal data are typically analyzed using frequency counts and percentages. For example, counting the number of males and females population or the frequency of different types of fruits sold in a specific region.

Ordinal data are categories with a natural order or ranking. Examples include survey ratings (poor, fair, good, excellent), educational levels (high school, college, graduate school), and socioeconomic status (low, middle, high). Ordinal data are used for ranking or ordering data, and they can be analyzed using median and mode, as well as non-parametric tests like the Mann-Whitney U test.

Quantitative Data

Quantitative data, also known as numerical data, consists of numbers representing quantities or measurements. Unlike qualitative data, which categorizes individuals or items based on attributes, quantitative data can be measured and expressed numerically, allowing for mathematical operations and statistical data analysis .

There are two types of Quantitative Data:

Discrete data are distinct, separate values that can be counted. Examples include the number of students in a class, the count of defects in a product, and the number of goals scored in a game. Discrete data are used for counting and tracking occurrences, and they can be analyzed using measures of central tendency such as mean and median, as well as discrete probability distributions like the Poisson distribution.

Continuous data can take any value within a range. Examples include height, weight, temperature, and time. Continuous data are used for measurements and observations, and they can be analyzed using mean and median, as well as continuous probability distributions like the normal distribution.

Difference Between Qualitative vs Quantitative Data

Quantitative and qualitative data exhibit significant differences. The fundamental distinctions are explored in the table below.

Examples of Qualitative Data

Some examples of qualitative data include:

Documents are a prevalent form of qualitative data, comprising materials like letters, diaries, blog posts, and digital images. These sources offer valuable insights into various research topics by providing firsthand accounts of individuals' thoughts and experiences. They are precious for understanding historical events, offering unique perspectives. When examining qualitative documents, you use a meticulous interpretation process to extract meaning from the text, considering its potential for multiple interpretations.

Case Studies

Case studies are frequently utilized qualitative research methodolody, involving detailed investigations into specific individuals, groups, or events. They offer insights into complex phenomena, shedding light on human thought processes, behaviors, and influencing factors. While valuable, case studies have limitations due to their reliance on a small sample size, potentially leading to a lack of representativeness and researcher bias.

Photographs

Photographs serve as a valuable form of qualitative data, providing insights into various visual aspects of human life, such as clothing, social interactions, and daily activities. They can also document changes over time, such as urban development or product evolution. Apart from their informational value, photographs can evoke emotions and visually capture human behavior complexities.

Audio Recordings

Audio recordings represent raw and unprocessed qualitative data, offering firsthand accounts of events or experiences. They capture spoken language nuances, emotions, and nonverbal cues, making them valuable for research purposes. Audio recordings are commonly used for interviews, focus groups, and studying naturalistic behaviors, albeit requiring meticulous analysis due to their complexity.

Examples of Quantitative Data

Some examples of quantitative data include:

Age in Years

Age commonly serves as a quantitative variable, often recorded in years. Whether precisely documented or categorized broadly (e.g., infancy, adolescence), age is a vital metric in various contexts. It can be represented continuously in units like days, weeks, or months or dichotomously to differentiate between child and adult age groups. Understanding age distribution facilitates demographic analysis and informs decisions across sectors like education and healthcare.

Height Measurement in Feet or Inches

Gathering quantitative data involves various methods. For instance, if you aim to measure the height of a group of individuals, you could utilize a tape measure, ruler, or yardstick to collect data in feet or inches. Once data is gathered, it can be used to compute the average height of the group and discern patterns or trends. For instance, you might observe correlations such as taller individuals tend to have higher weights or gender disparities in average height. Quantitative data proves invaluable for comprehending human behavior and making informed predictions.

Number of Weeks in a Year

A year comprises 52 weeks, providing a precise and measurable quantity, which exemplifies quantitative data. This type of data is crucial in scientific research because the number of weeks allows for standardized comparisons across studies.  

For instance, you can track changes in a population's health over 52 weeks (a year) and compare those findings to studies that measured health changes over 26 weeks (half a year). This consistency in measurement enables the identification of trends and relationships between variables more effectively, leading to insightful analyses.

Revenue in Dollars

Quantitative data, which is numerical and measurable, encompasses metrics like revenue expressed in any form of currency. This data type proves invaluable for assessing various aspects, such as a company's financial performance, products sold on a website and its traffic volume, or product sales quantity. The data is commonly gathered through surveys, experiments, or data analysis, enabling statistical methods to discern trends and correlations.

Distance in Kilometers

Distance measurement stands as another quintessential example of quantitative data, with kilometers being the universally accepted unit for long distances. Kilometers provide a manageable scale for expressing distances without necessitating unwieldy numbers. For instance, kilometers offer a convenient and widely understood metric when measuring the distance from a source to destination.

Since statistical analysis hinges on a unified data set, Airbyte can help you bridge the gap. It effortlessly allows you to gather and centralize information, eliminating the hassle of data collection.

Simplify Statistical Data Analysis with Airbyte

Airbyte , a data integration platform, simplifies the process of integrating and replicating data from various sources. Once centralized, this data empowers statisticians to perform in-depth analysis. By eliminating manual data transfer and ensuring consistent data flow, Airbyte saves valuable time and resources. This allows them to focus on what matters most—extracting meaningful insights from the data.

Here’s what Airbyte offers:

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• Monitoring and Integrations: Airbyte allows you to seamlessly integrate with monitoring platforms like Datadog, enabling you to keep track of data pipeline health and performance. Additionally, it supports integrations with popular workflow orchestration tools like Airflow, Prefect, and Dagster for streamlined data pipeline management and processing. ‍
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• PyAirbyte: PyAirbyte , a Python library, lets you programmatically interact with Airbyte's vast library of pre-built connectors. This allows you to automate data integration tasks and leverage Airbyte's extensive functionality through code.

Data and types of data in statistics are significant as they aid in understanding global phenomena and guiding your decision-making process. Statistics data encompass various data types, each with its use cases. However, by comprehending these data types, you can utilize them effectively to obtain the most accurate insights possible.

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Types of Data in Statistics

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What are Types of Data in Statistics?

The data is classified into majorly four categories:

• Nominal data
• Ordinal data
• Discrete data
• Continuous data

Further, we can classify these data as follows:

Let us discuss the different types of data in Statistics herewith examples.

Qualitative or Categorical Data

Qualitative data, also known as the categorical data , describes the data that fits into the categories. Qualitative data are not numerical. The categorical information involves categorical variables that describe the features such as a person’s gender, home town etc. Categorical measures are defined in terms of natural language specifications, but not in terms of numbers.

Sometimes categorical data can hold numerical values (quantitative value), but those values do not have a mathematical sense. Examples of the categorical data are birthdate, favourite sport, school postcode. Here, the birthdate and school postcode hold the quantitative value, but it does not give numerical meaning.

Nominal Data

Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value. Nominal data is also called the nominal scale. It cannot be ordered and measured. But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender etc.

The nominal data are examined using the grouping method. In this method, the data are grouped into categories, and then the frequency or the percentage of the data can be calculated. These data are visually represented using the pie charts.

Ordinal Data

Ordinal data/variable is a type of data that follows a natural order. The significant feature of the nominal data is that the difference between the data values is not determined. This variable is mostly found in surveys, finance, economics, questionnaires, and so on.

The ordinal data is commonly represented using a bar chart. These data are investigated and interpreted through many visualisation tools. The information may be expressed using tables in which each row in the table shows the distinct category.

Quantitative or Numerical Data

Quantitative data is also known as numerical data which represents the numerical value (i.e., how much, how often, how many). Numerical data gives information about the quantities of a specific thing. Some examples of numerical data are height, length, size, weight, and so on. The quantitative data can be classified into two different types based on the data sets . The two different classifications of numerical data are discrete data and continuous data.

Discrete Data

Discrete data can take only discrete values. Discrete information contains only a finite number of possible values. Those values cannot be subdivided meaningfully. Here, things can be counted in whole numbers.

Example: Number of students in the class

Continuous Data

Continuous data is data that can be calculated. It has an infinite number of probable values that can be selected within a given specific range.

Example: Temperature range

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