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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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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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Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

• The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on.  Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
• Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

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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.

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• Korean J Anesthesiol
• v.70(3); 2017 Jun

Statistical data presentation

1 Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea.

Sangseok Lee

2 Department of Anesthesiology and Pain Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea.

Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed, and analyzed. However, no matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers. In this article, the techniques of data and information presentation in textual, tabular, and graphical forms are introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole. Text, tables, and graphs for data and information presentation are very powerful communication tools. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.

Introduction

Data are a set of facts, and provide a partial picture of reality. Whether data are being collected with a certain purpose or collected data are being utilized, questions regarding what information the data are conveying, how the data can be used, and what must be done to include more useful information must constantly be kept in mind.

Since most data are available to researchers in a raw format, they must be summarized, organized, and analyzed to usefully derive information from them. Furthermore, each data set needs to be presented in a certain way depending on what it is used for. Planning how the data will be presented is essential before appropriately processing raw data.

First, a question for which an answer is desired must be clearly defined. The more detailed the question is, the more detailed and clearer the results are. A broad question results in vague answers and results that are hard to interpret. In other words, a well-defined question is crucial for the data to be well-understood later. Once a detailed question is ready, the raw data must be prepared before processing. These days, data are often summarized, organized, and analyzed with statistical packages or graphics software. Data must be prepared in such a way they are properly recognized by the program being used. The present study does not discuss this data preparation process, which involves creating a data frame, creating/changing rows and columns, changing the level of a factor, categorical variable, coding, dummy variables, variable transformation, data transformation, missing value, outlier treatment, and noise removal.

We describe the roles and appropriate use of text, tables, and graphs (graphs, plots, or charts), all of which are commonly used in reports, articles, posters, and presentations. Furthermore, we discuss the issues that must be addressed when presenting various kinds of information, and effective methods of presenting data, which are the end products of research, and of emphasizing specific information.

Data Presentation

Data can be presented in one of the three ways:

–as text;

–in tabular form; or

–in graphical form.

Methods of presentation must be determined according to the data format, the method of analysis to be used, and the information to be emphasized. Inappropriately presented data fail to clearly convey information to readers and reviewers. Even when the same information is being conveyed, different methods of presentation must be employed depending on what specific information is going to be emphasized. A method of presentation must be chosen after carefully weighing the advantages and disadvantages of different methods of presentation. For easy comparison of different methods of presentation, let us look at a table ( Table 1 ) and a line graph ( Fig. 1 ) that present the same information [ 1 ]. If one wishes to compare or introduce two values at a certain time point, it is appropriate to use text or the written language. However, a table is the most appropriate when all information requires equal attention, and it allows readers to selectively look at information of their own interest. Graphs allow readers to understand the overall trend in data, and intuitively understand the comparison results between two groups. One thing to always bear in mind regardless of what method is used, however, is the simplicity of presentation.

Values are expressed as mean ± SD. Group C: normal saline, Group D: dexmedetomidine. SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, HR: heart rate. * P < 0.05 indicates a significant increase in each group, compared with the baseline values. † P < 0.05 indicates a significant decrease noted in Group D, compared with the baseline values. ‡ P < 0.05 indicates a significant difference between the groups.

Text presentation

Text is the main method of conveying information as it is used to explain results and trends, and provide contextual information. Data are fundamentally presented in paragraphs or sentences. Text can be used to provide interpretation or emphasize certain data. If quantitative information to be conveyed consists of one or two numbers, it is more appropriate to use written language than tables or graphs. For instance, information about the incidence rates of delirium following anesthesia in 2016–2017 can be presented with the use of a few numbers: “The incidence rate of delirium following anesthesia was 11% in 2016 and 15% in 2017; no significant difference of incidence rates was found between the two years.” If this information were to be presented in a graph or a table, it would occupy an unnecessarily large space on the page, without enhancing the readers' understanding of the data. If more data are to be presented, or other information such as that regarding data trends are to be conveyed, a table or a graph would be more appropriate. By nature, data take longer to read when presented as texts and when the main text includes a long list of information, readers and reviewers may have difficulties in understanding the information.

Table presentation

Tables, which convey information that has been converted into words or numbers in rows and columns, have been used for nearly 2,000 years. Anyone with a sufficient level of literacy can easily understand the information presented in a table. Tables are the most appropriate for presenting individual information, and can present both quantitative and qualitative information. Examples of qualitative information are the level of sedation [ 2 ], statistical methods/functions [ 3 , 4 ], and intubation conditions [ 5 ].

The strength of tables is that they can accurately present information that cannot be presented with a graph. A number such as “132.145852” can be accurately expressed in a table. Another strength is that information with different units can be presented together. For instance, blood pressure, heart rate, number of drugs administered, and anesthesia time can be presented together in one table. Finally, tables are useful for summarizing and comparing quantitative information of different variables. However, the interpretation of information takes longer in tables than in graphs, and tables are not appropriate for studying data trends. Furthermore, since all data are of equal importance in a table, it is not easy to identify and selectively choose the information required.

For a general guideline for creating tables, refer to the journal submission requirements 1) .

Heat maps for better visualization of information than tables

Heat maps help to further visualize the information presented in a table by applying colors to the background of cells. By adjusting the colors or color saturation, information is conveyed in a more visible manner, and readers can quickly identify the information of interest ( Table 2 ). Software such as Excel (in Microsoft Office, Microsoft, WA, USA) have features that enable easy creation of heat maps through the options available on the “conditional formatting” menu.

All numbers were created by the author. SBP: systolic blood pressure, DBP: diastolic blood pressure, MBP: mean blood pressure, HR: heart rate.

Graph presentation

Whereas tables can be used for presenting all the information, graphs simplify complex information by using images and emphasizing data patterns or trends, and are useful for summarizing, explaining, or exploring quantitative data. While graphs are effective for presenting large amounts of data, they can be used in place of tables to present small sets of data. A graph format that best presents information must be chosen so that readers and reviewers can easily understand the information. In the following, we describe frequently used graph formats and the types of data that are appropriately presented with each format with examples.

Scatter plot

Scatter plots present data on the x - and y -axes and are used to investigate an association between two variables. A point represents each individual or object, and an association between two variables can be studied by analyzing patterns across multiple points. A regression line is added to a graph to determine whether the association between two variables can be explained or not. Fig. 2 illustrates correlations between pain scoring systems that are currently used (PSQ, Pain Sensitivity Questionnaire; PASS, Pain Anxiety Symptoms Scale; PCS, Pain Catastrophizing Scale) and Geop-Pain Questionnaire (GPQ) with the correlation coefficient, R, and regression line indicated on the scatter plot [ 6 ]. If multiple points exist at an identical location as in this example ( Fig. 2 ), the correlation level may not be clear. In this case, a correlation coefficient or regression line can be added to further elucidate the correlation.

Bar graph and histogram

A bar graph is used to indicate and compare values in a discrete category or group, and the frequency or other measurement parameters (i.e. mean). Depending on the number of categories, and the size or complexity of each category, bars may be created vertically or horizontally. The height (or length) of a bar represents the amount of information in a category. Bar graphs are flexible, and can be used in a grouped or subdivided bar format in cases of two or more data sets in each category. Fig. 3 is a representative example of a vertical bar graph, with the x -axis representing the length of recovery room stay and drug-treated group, and the y -axis representing the visual analog scale (VAS) score. The mean and standard deviation of the VAS scores are expressed as whiskers on the bars ( Fig. 3 ) [ 7 ].

By comparing the endpoints of bars, one can identify the largest and the smallest categories, and understand gradual differences between each category. It is advised to start the x - and y -axes from 0. Illustration of comparison results in the x - and y -axes that do not start from 0 can deceive readers' eyes and lead to overrepresentation of the results.

One form of vertical bar graph is the stacked vertical bar graph. A stack vertical bar graph is used to compare the sum of each category, and analyze parts of a category. While stacked vertical bar graphs are excellent from the aspect of visualization, they do not have a reference line, making comparison of parts of various categories challenging ( Fig. 4 ) [ 8 ].

A pie chart, which is used to represent nominal data (in other words, data classified in different categories), visually represents a distribution of categories. It is generally the most appropriate format for representing information grouped into a small number of categories. It is also used for data that have no other way of being represented aside from a table (i.e. frequency table). Fig. 5 illustrates the distribution of regular waste from operation rooms by their weight [ 8 ]. A pie chart is also commonly used to illustrate the number of votes each candidate won in an election.

Line plot with whiskers

A line plot is useful for representing time-series data such as monthly precipitation and yearly unemployment rates; in other words, it is used to study variables that are observed over time. Line graphs are especially useful for studying patterns and trends across data that include climatic influence, large changes or turning points, and are also appropriate for representing not only time-series data, but also data measured over the progression of a continuous variable such as distance. As can be seen in Fig. 1 , mean and standard deviation of systolic blood pressure are indicated for each time point, which enables readers to easily understand changes of systolic pressure over time [ 1 ]. If data are collected at a regular interval, values in between the measurements can be estimated. In a line graph, the x-axis represents the continuous variable, while the y-axis represents the scale and measurement values. It is also useful to represent multiple data sets on a single line graph to compare and analyze patterns across different data sets.

Box and whisker chart

A box and whisker chart does not make any assumptions about the underlying statistical distribution, and represents variations in samples of a population; therefore, it is appropriate for representing nonparametric data. AA box and whisker chart consists of boxes that represent interquartile range (one to three), the median and the mean of the data, and whiskers presented as lines outside of the boxes. Whiskers can be used to present the largest and smallest values in a set of data or only a part of the data (i.e. 95% of all the data). Data that are excluded from the data set are presented as individual points and are called outliers. The spacing at both ends of the box indicates dispersion in the data. The relative location of the median demonstrated within the box indicates skewness ( Fig. 6 ). The box and whisker chart provided as an example represents calculated volumes of an anesthetic, desflurane, consumed over the course of the observation period ( Fig. 7 ) [ 9 ].

Three-dimensional effects

Most of the recently introduced statistical packages and graphics software have the three-dimensional (3D) effect feature. The 3D effects can add depth and perspective to a graph. However, since they may make reading and interpreting data more difficult, they must only be used after careful consideration. The application of 3D effects on a pie chart makes distinguishing the size of each slice difficult. Even if slices are of similar sizes, slices farther from the front of the pie chart may appear smaller than the slices closer to the front ( Fig. 8 ).

Drawing a graph: example

Finally, we explain how to create a graph by using a line graph as an example ( Fig. 9 ). In Fig. 9 , the mean values of arterial pressure were randomly produced and assumed to have been measured on an hourly basis. In many graphs, the x- and y-axes meet at the zero point ( Fig. 9A ). In this case, information regarding the mean and standard deviation of mean arterial pressure measurements corresponding to t = 0 cannot be conveyed as the values overlap with the y-axis. The data can be clearly exposed by separating the zero point ( Fig. 9B ). In Fig. 9B , the mean and standard deviation of different groups overlap and cannot be clearly distinguished from each other. Separating the data sets and presenting standard deviations in a single direction prevents overlapping and, therefore, reduces the visual inconvenience. Doing so also reduces the excessive number of ticks on the y-axis, increasing the legibility of the graph ( Fig. 9C ). In the last graph, different shapes were used for the lines connecting different time points to further allow the data to be distinguished, and the y-axis was shortened to get rid of the unnecessary empty space present in the previous graphs ( Fig. 9D ). A graph can be made easier to interpret by assigning each group to a different color, changing the shape of a point, or including graphs of different formats [ 10 ]. The use of random settings for the scale in a graph may lead to inappropriate presentation or presentation of data that can deceive readers' eyes ( Fig. 10 ).

Owing to the lack of space, we could not discuss all types of graphs, but have focused on describing graphs that are frequently used in scholarly articles. We have summarized the commonly used types of graphs according to the method of data analysis in Table 3 . For general guidelines on graph designs, please refer to the journal submission requirements 2) .

Conclusions

Text, tables, and graphs are effective communication media that present and convey data and information. They aid readers in understanding the content of research, sustain their interest, and effectively present large quantities of complex information. As journal editors and reviewers will scan through these presentations before reading the entire text, their importance cannot be disregarded. For this reason, authors must pay as close attention to selecting appropriate methods of data presentation as when they were collecting data of good quality and analyzing them. In addition, having a well-established understanding of different methods of data presentation and their appropriate use will enable one to develop the ability to recognize and interpret inappropriately presented data or data presented in such a way that it deceives readers' eyes [ 11 ].

<Appendix>

Output for presentation.

Discovery and communication are the two objectives of data visualization. In the discovery phase, various types of graphs must be tried to understand the rough and overall information the data are conveying. The communication phase is focused on presenting the discovered information in a summarized form. During this phase, it is necessary to polish images including graphs, pictures, and videos, and consider the fact that the images may look different when printed than how appear on a computer screen. In this appendix, we discuss important concepts that one must be familiar with to print graphs appropriately.

The KJA asks that pictures and images meet the following requirement before submission 3)

“Figures and photographs should be submitted as ‘TIFF’ files. Submit files of figures and photographs separately from the text of the paper. Width of figure should be 84 mm (one column). Contrast of photos or graphs should be at least 600 dpi. Contrast of line drawings should be at least 1,200 dpi. The Powerpoint file (ppt, pptx) is also acceptable.”

Unfortunately, without sufficient knowledge of computer graphics, it is not easy to understand the submission requirement above. Therefore, it is necessary to develop an understanding of image resolution, image format (bitmap and vector images), and the corresponding file specifications.

Resolution is often mentioned to describe the quality of images containing graphs or CT/MRI scans, and video files. The higher the resolution, the clearer and closer to reality the image is, while the opposite is true for low resolutions. The most representative unit used to describe a resolution is “dpi” (dots per inch): this literally translates to the number of dots required to constitute 1 inch. The greater the number of dots, the higher the resolution. The KJA submission requirements recommend 600 dpi for images, and 1,200 dpi 4) for graphs. In other words, resolutions in which 600 or 1,200 dots constitute one inch are required for submission.

There are requirements for the horizontal length of an image in addition to the resolution requirements. While there are no requirements for the vertical length of an image, it must not exceed the vertical length of a page. The width of a column on one side of a printed page is 84 mm, or 3.3 inches (84/25.4 mm ≒ 3.3 inches). Therefore, a graph must have a resolution in which 1,200 dots constitute 1 inch, and have a width of 3.3 inches.

Bitmap and Vector

Methods of image construction are important. Bitmap images can be considered as images drawn on section paper. Enlarging the image will enlarge the picture along with the grid, resulting in a lower resolution; in other words, aliasing occurs. On the other hand, reducing the size of the image will reduce the size of the picture, while increasing the resolution. In other words, resolution and the size of an image are inversely proportionate to one another in bitmap images, and it is a drawback of bitmap images that resolution must be considered when adjusting the size of an image. To enlarge an image while maintaining the same resolution, the size and resolution of the image must be determined before saving the image. An image that has already been created cannot avoid changes to its resolution according to changes in size. Enlarging an image while maintaining the same resolution will increase the number of horizontal and vertical dots, ultimately increasing the number of pixels 5) of the image, and the file size. In other words, the file size of a bitmap image is affected by the size and resolution of the image (file extensions include JPG [JPEG] 6) , PNG 7) , GIF 8) , and TIF [TIFF] 9) . To avoid this complexity, the width of an image can be set to 4 inches and its resolution to 900 dpi to satisfy the submission requirements of most journals [ 12 ].

Vector images overcome the shortcomings of bitmap images. Vector images are created based on mathematical operations of line segments and areas between different points, and are not affected by aliasing or pixelation. Furthermore, they result in a smaller file size that is not affected by the size of the image. They are commonly used for drawings and illustrations (file extensions include EPS 10) , CGM 11) , and SVG 12) ).

Finally, the PDF 13) is a file format developed by Adobe Systems (Adobe Systems, CA, USA) for electronic documents, and can contain general documents, text, drawings, images, and fonts. They can also contain bitmap and vector images. While vector images are used by researchers when working in Powerpoint, they are saved as 960 × 720 dots when saved in TIFF format in Powerpoint. This results in a resolution that is inappropriate for printing on a paper medium. To save high-resolution bitmap images, the image must be saved as a PDF file instead of a TIFF, and the saved PDF file must be imported into an imaging processing program such as Photoshop™(Adobe Systems, CA, USA) to be saved in TIFF format [ 12 ].

1) Instructions to authors in KJA; section 5-(9) Table; https://ekja.org/index.php?body=instruction

2) Instructions to Authors in KJA; section 6-1)-(10) Figures and illustrations in Manuscript preparation; https://ekja.org/index.php?body=instruction

3) Instructions to Authors in KJA; section 6-1)-(10) Figures and illustrations in Manuscript preparation; https://ekja.org/index.php?body=instruction

4) Resolution; in KJA, it is represented by “contrast.”

5) Pixel is a minimum unit of an image and contains information of a dot and color. It is derived by multiplying the number of vertical and horizontal dots regardless of image size. For example, Full High Definition (FHD) monitor has 1920 × 1080 dots ≒ 2.07 million pixel.

6) Joint Photographic Experts Group.

7) Portable Network Graphics.

8) Graphics Interchange Format

9) Tagged Image File Format; TIFF

10) Encapsulated PostScript.

11) Computer Graphics Metafile.

12) Scalable Vector Graphics.

13) Portable Document Format.

10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

• 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation with AI nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

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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Skills to Develop

• 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:

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

or in set notation as:

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

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

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

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.

Contributor

• Template:ContribShaferZhang

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).

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

Leah Nguyen • 05 April, 2024 • 17 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

#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.

Tips to Engage with Polls & Trivia

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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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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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.

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[Updated 2023] Top 50 Data and Statistics PowerPoint Templates Used by Analysts Worldwide!

Deepali Khatri

“Without data, you’re just another person with an opinion.” – Late W. Edwards Deming, a leading management thinker and authority in the field of quality.

Data, like pictures, tell stories. They can make people pause and think about a problem, rally people for a cause, and convince people with logic and hard facts. A presentation is all about persuasion; for this, you must present data visually appealingly.

One of the most popular ways to share your data with your audience is infographics. These make your presentation eye-catching and more engaging.

RELATED READ: Top 10 One Page Data and Statistics Templates To Make Your Business Decisions More Prominent

Creating visual content in marketing is not a piece of cake. This is why we take all the pain and bring high-grade data and statistics templates, be it charts, graphs, or other data visualization, to help you present your data in a way the audience can easily understand.

Whether it is market trend analysis, company performance, summary statistics, social media usage, or any other topic, we offer you predesigned, fully editable (excel-linked) data PPT templates to evaluate and analyze data that will further help you in decision-making. All you have to do is replace the sample data with your data, and your presentation will be ready in no time.

Here we present the top 40 data and statistics PowerPoint templates that will assist you in interpreting data more efficiently and easily.

Statistics PowerPoint PPT Template Bundles -1

Click Here to Get This Statistics PowerPoint PPT Template Bundles

This presentation template provides the framework for conducting and inserting your data in this wonderfully-designed comprehensive, complete deck. The structure is provided, with the title on each slide giving full details on what will be added. For instance, a graphical representation of data using statistical measures is one such slide; a financial analysis slide will add that element of authority to your presentation. Statistical icons on investment marketing, poll graph, etc., provide that much-needed variety and showcase your expertise. Download this template now to make a real impact!

Social Media Key Statistics PPT Professional Files -2

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With social media the rage, it is critical for businesses to know they're two-bit about the platform and take steps to ensure that they can gain from it. Use this presentation template to know about the parameters that dictate social media penetration in terms of the standard business information that companies look for, like urbanization, penetration of social media among segments of the population (categorized according to age or income or any other relevant parameter to your line of business). The number of internet users and the percentage of active social media users are also documented. Unique mobile users on social media and active mobile social users (all from the population in your market) can be represented to help you plan your product launch or the next big marketing push. Download this template now to get a feel of harnessing the social media phenomenon for your business needs.

Data Presentation Statistics Example of PPT Presentation-3

Get This Pre-Designed Data Presentation Statistics Example of PPT Presentation

This year-wise PPT Presentation helps you depict data on say sales or profit percentages or growth in a particular product category. The template is designed to ensure that a top management personnel can understand the business trajectory and suggest remedial measures. For now, the scale is 1-100, but at the cost of repetition, please bear in mind that each of these templates is 100% customizable and editable. If your scale is different, it will take just a click to set this template up that way. Download now to start preparing for your next big data, presentation now!

Statistics PPT Template with Demographics Market Segment-4

Click Here to Download the Statistics PPT Template with Demographics Market Segment

Use this PPT Presentation to highlight how market segments and the demography of customers interact to provide you with the perfect potion for business success. If you do not win, you can analyze this data to arrive at a meaningful strategy for your business. The color codes help you either have four markets or four demographic segments and understand their interaction (What’s even more fabulous is that the templates are editable so that you can add your legends as well). Download this template to showcase your expertise in how statistics and data can be visually represented to tell a story for you to take action on and make an intelligent business decision.

Statistical Analysis PowerPoint Presentation Slides-5

Grab this Customizable Statistical Analysis PowerPoint Layout

Analyze the business data and its effectiveness through this amazingly curated statistical analysis PowerPoint graphic. This deck consists of 30 slides that let you examine and analyze the data to reach an effective business decision. Showcase the statistical results and predictive analytics benefits. Define various processes and showcase how data is collected, integrated and analyzed using this customizable statistical analysis template.

Online Travelling Stats PowerPoint Guide-6

Download this Easy-To-Use Online Travelling Stats PowerPoint Layout

Prepare your travel plans by comparing various parameters involved in tours and execute the best deal. Conduct a comparative analysis between the deals offered by various websites. Describe your concept and interact with your market for customer acquisition. Group your ideas at one place via this amazingly designed online traveling stats PPT guide.

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Expand your market share by boosting up the sales of your product by incorporating this readily available social media PowerPoint slide design. Conduct a proper analysis of the revenue generated from these social media sites. Social media is an essential marketing tool for marketers to promote their brand. Understand the social media behavior of your target audience with this editable doughnut chart template. It can be viewed on a standard screen and a widescreen as well. Click on the download button now and grab this template now.

Improve Statistics Sample Presentation PPT-8

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Download this improve statistics template to improve your business value. Analyze your business information using this slide that will enable you to take effective decisions. The slide will help you analyze your business stock market which lets you make a better and informed investment decision. The readymade PPT template can also be used in the areas of finance, marketing, research and development.

Product Usage Facebook Stats PowerPoint Ideas-9

Get this amazingly curated product usage Facebook stats PPT template

This slide consists of a man’s portrait that can be used to showcase various parameters with percentages. The product usage Facebook stats slide can also be used to depict the traffic derived from Facebook for your brand. Also, you can easily segregate the customers based on their buying pattern, consumption pattern, demographic and other factors and present it front of your viewers for better understanding.

Content Management System With Statistics Documentation DataBase Ideas And Business-10

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The CMS PPT template will let you manage and organize web content. Share entire process of managing content and mention the steps. You can easily plan, develop, manage, preserve and evaluate the content within an organization. This PPT slide will let you create, manage and modify content on the website without any requirement of specialized technical knowledge. Also, taking the assistance of this template one can keep the content updated. Guide your workforce the ways and techniques they should adopt to manage content efficiently.

Limitations of Statistics PPT Design-11

Get this Readily Available limitation of Statistics PPT Slide Show

Showcase the business limitation of statistics using this 8 stage limitations of statistics PPT slide. Display various aspects hindering your outcomes and familiarize your employees and subordinates with different problem-solving techniques. Devise strategies to overcome the limitations and present the plans formulated in front of your viewers.

Population Demographic Statistics Bar Chart-12

Download this Pre-Designed Population Demographic Statistics Bar Chart PowerPoint Example

This PPT template will let you display data set as a breakdown of males and females. Depict the total number of males and females working in your business organization. An increase in the population of a particular area can also be displayed taking advantage of this readily available demography statistics bar chart template.

Demand Statistics PowerPoint Slide Themes-13

This demand statistics PowerPoint slide design will let you predict the demand of goods and services. Analyze the demand patterns over the past few years and make production estimates accordingly. A clear estimate will help you in reducing the risks involved in business activity. Make important business decisions by forecasting the demand through this professionally curated demand statistics PPT layout.

Statistics Results PPT Inspiration Gallery-14

Download this Amazingly Designed Statistics Results PPT Guide

The template consists of two windows, one consisting of a bar graph and the other consisting of circular diagram depicting some percentage rates associated with different categories. Complex data can be easily represented via this easy to use PPT slide design. Professionals from various backgrounds can incorporate this slide to deliver impactful presentations.

Retention Acquisition Statistics PowerPoint Images-15

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Improve the customer satisfaction rate and showcase the same using this retention acquisition statistics template. The acquisition statistics PPT slide will let you gain and retain more customers. Define various benefits of customer acquisition via this creatively designed PowerPoint template. Also, the slide helps in building result-oriented business marketing techniques for your company.

Data Driven 3D Pie Chart For Business Statistics PowerPoint Slides-16

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Showcase the size of market segments your organization is dealing in. display the company’s performance in different quarters. Present the revenue generated from various segments. This template can also be used for allocating budget and for comparing profit percentages of different products. Provide a clear understanding of various concepts with this statistical tool rather than explanative documents.

Bar Graph For Year Based Chart And Financial Details Flat PowerPoint Design-17

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Incorporate this template for finance and marketing related presentations. Portray business or financial strategy, growth patterns and other financial aspects using this professionally designed growth graph statistics PPT layout. You can also employ this slide to track liquidity, budget, expenses and cash flows. Set valuable financial goals that result in growth and success.

 Donut Chart with Icons for Data Driven Statistics-18

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Represent percentage or numerical proportions of the data with the help of this predesigned donut chart icon for data-driven statistics. Display the relative size of the market segment your organization is dealing with. This slide can also be incorporated to depict the relationship of different parts to the whole. Compare the profit percentage of different products that your organization is selling. Display the data, facts and numerical proportions in an organized way via this amazingly designed customizable donut chart PPT slide show.

Employee Engagement Statistics PowerPoint Slide Rules-19

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This employee engagement PowerPoint template will let you showcase employee’s satisfaction with data driven approach. Our designers at SlideTeam have created this template to keep you updated of latest market trends. Employ this PowerPoint layout to showcase your employees the significance of their contributions. Incorporate the template to present the ways to improve productivity. Enrich the work experience for employees taking the advantage of this customizable employee engagement statistics PPT slide.

Trends Statistics Diagram Sample Presentation PPT-20

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Provide an accurate estimate about costs, demand, sales and price and make a sound decision by incorporating trends statistics diagram PPT template. The slide will help the user make a report on business planning based on the predictions and assumptions. The slide can also be used in the field of science for the purpose of taking sound decisions and for finding out patterns in the given data.

Video Marketing Statistics Template Presentation Layouts-21

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Increase the engagement of your audience on digital and social media channels taking the assistance of this readymade PowerPoint slide design. This PPT slide will let you grab the attention of your viewers and will also assist you in boosting up the conversion rate. Showcase the benefits and service you provide to the customers taking the advantage of this content ready PPT layout.

Data-Driven 3D Bar Chart for Research In Statistics PowerPoint Slides-22

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Present your project stats in front of your team members with the help of this editable data driven 3D bar chart for research in statistics template. Highlight various metrics that will assist you in showcasing your improving or dropping stats of the project. Presentations on topics like research and development, research management, technological and product development data can be conveniently delivered.

Financial Sales Growth Chart -23

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Develop a formal record of financial activities of your business organization and make a financial statement with the help of this professionally designed financial sales growth chart PPT template. Add the required details for financial statements in just single slide and present it in front of your audience. Compile the list of financial statements like the balance sheet, cash flow statement, and income statement and present it in a format that can be easily understood by the viewers.

Project Status Kpi Dashboard Showing Portfolio Statistics And Workflow Phase-24

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Convey your message graphically with this six-stage presentation PPT slide. This project status PPT template can be employed to deal with the topics like project health card, project status, project performance, etc. this template consists of bar chart graphs and pie charts that can be used to depict workflow phases, project planning and execution, initiation of a project, and other similar topics.

Business Strategy Consultant Growth Bar Chart Powerpoint Templates -25

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Display the growth patterns of your business organization using this business growth bar chart PowerPoint template. This slide will let you measure change over time. One can also devise a strategy to reach the set targets and goals. Mention various steps one needs to follow to accomplish desired goals. Incorporate the template and provide a clear understanding of the formulated plans to your audience.

Four Staged Pricing Table With Right And Wrong Symbol Flat PowerPoint Design-26

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Achieve consistency in pricing practices and keep a check on price with the help of this four staged pricing table with right and wrong symbol PowerPoint slide design. Manage your menu strategy and showcase the different product items of your organization and their prices. Present the pricing of various products or service offerings available to customers. Portray the prices at which the product is sold in different market segments using this pricing table PPT template.

Skill Matrix Report Presentation Slide PPT Diagrams-27

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Manage your organization’s human resource by incorporating this skill matrix report PPT slide. Represent your analysis effectively and convey the message in an organized manner. Display the assessment of a company’s workforce in areas like communication, leadership, self-development, job responsibility, critical thinking, decision making, etc. This nine-stage process template is best tool to analyze skills of employees in various fields.

Business Performance Dashboards With New Customers And Gross Profit-28

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The template consists of a chart, bar graph, pie chart and map that can be used as the best tool to represent the company’s performance. Explain the techniques of achieving the goals and the timeframe within which these can be achieved. You can also insert your own text in the slide. Analyze and evaluate the areas where you need improvement taking the advantage of this business performance dashboard PowerPoint slide show.

Bar Graph With Business Analysis Icons Flat PowerPoint Design-29

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Have a balance report of business performance through this creatively designed bar graph PowerPoint diagram. Evaluate organizational performance in different market segments and devise strategies on the basis of evaluation. Present the profits or revenue earned from different products using this easy-to-use editable PowerPoint slide show.

Multiple Charts Showcasing Email Marketing Analysis Presentation Slides-30

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Analyze the success of your email marketing by evaluating its performance through this amazingly curated multiple chart PowerPoint layout. Also, the template assists you in analyzing the effectiveness of your marketing campaign. The template consists of conversion rate, bounce rate, unsubscribe rate. This slide will let you improve the ROI of your marketing campaign. Develop effective and result oriented business strategies for your business organization.

Year In Review Business PowerPoint Ideas-31

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Evaluate the activities of the business organization done in the past years. Have a complete overview of the business and get to know where your business is heading towards. Display the number of assets your organization had in the past years. Remove the sample text and insert your own text in place in this 100% editable PowerPoint slide show. Inform your audience about the total taxes paid and the revenue of your business organization.

Pie Chart And Line Chart Data-Driven Analysis PowerPoint Slides-32

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Deliver impactful PPT presentation on business and marketing related topics with the assistance of this professionally designed PowerPoint slide show. The template consists of a line chart and a pie chart that can be used to present various numerical proportions. Showcase the market trend analysis using this customizable PowerPoint layout. Draw out conclusions for your business requirement with this easy to understand PPT charts.

Business Butterfly Bar Chart PowerPoint Graph-33

Download this Business Butterfly Chart PowerPoint Graph

The template can be used to make comparison in organizational data. Employ the slide to compare different products your organization is dealing in or compare two different market segments. Conducting a proper comparison will help you focus on the key areas that need improvement. Conduct a comparative analysis of your profits of different years and find out the ways you can increase the profits.

Column Chart PowerPoint Layout-34

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Display change over time by comparing column length via this amazingly designed column chart PPT layout. Incorporate this template to represent categorical data. Demonstrate different categories of sales and the performance of different products over the past few years. This rectangular bar chart PPT slide show can also be utilized for making a comparison in the sales report.

Multiple Charts For Business Growth Presentation Images-35

Click Here to Download Multiple Charts for Business Growth Presentation Template

This is a 2-stage process template consisting of donut charts and bar charts for business growth. Display the growth of your business organization taking the assistance of this visually appealing multiple charts PowerPoint template. Use this process and convey the concept of multiple charts in a way that can be easily understood by the audience. Compare your performance with that of your competitors and devise strategies to overcome the hurdles in your way to success.

Project Activity Gantt Chart Timeline-36

Download this Project Activity Gantt Chart Timeline PPT Slide

Showcase the timeline for your business activities on weekly basis. This is an easy way to schedule your activities and track the progress of your project. Project managers can make use of this project activity PPT layout for keeping the track of business activities. Elucidate the time taken to complete one task and display the milestones that are to be achieved. List down the activities on the basis of their priority and guide your workforce the ways to complete them.

Multiple Charts For Sales Dashboard Presentation Pictures-37

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This technically designed multiple charts for sale dashboard PPT template will let you evaluate your sales performance. Analyze how the business entity is performing from past few years. Find out the possible solutions to enhance organizational productivity and company’s sales. Monitors your employees’ performance for achieving the set targets. Identify the high and low performing sales team and employees through this sales metrics dashboard.

Multiple Charts Sample Presentation PPT-38

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Reduce your real business expenses and find out the ways of increasing profits by incorporating this multiple charts PowerPoint slide show. This template consists of charts, graphs, grids and tables, etc. identify the sales prices for different clients and the percentage each one is contributing to overall sales. Display the number of orders by different clients with this engaging charts PPT slide.

Business Sales Stats Sample Of PPT-39

Download this Business Sales Stats Sample of PPT Slide

Present the data related to business sales using this readily available business sales statistics of PPT. data presented using such bar chart template can be analyzed quickly. Get a clear idea of the company’s sales. One can easily analyze how a particular product is performing in various market segments. Evaluate the collected information in a standardized way.

Data-Driven Line Chart Diagram PowerPoint Slides-40

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Showcase the conclusion of a project that has been initiated in the past. This line chart PPT template can also be used by the network, marketing, and production companies. Depict the complete information of a project along with the ups and downs. One can also incorporate the template to compare the sales of three different products and can easily find out which product is performing well.

These are the 40 best templates for data and statistics that can assist you in your next project or presentation. Go for the template you like the most!

FAQs on Data and Statistics

What is data and statistics used for.

Data and Statistics are a profound body of knowledge and a research tool that has only recently come into its own due to the proliferation of computers, artificial intelligence and the need for big data analytics. In essence, data is a piece of information about the world that is usually numerical (it can be qualitative as well) and lets us in on how large, small, huge or little things are. Statistics is the application of mathematical tools and analysis on data to derive conclusions that have everyday applications and meaning, after representing it visually as well. Statistics also helps us decide or predict what the next set of data will look like.

What is the best way to present statistics?

The best way to present statistics is through a business dashboard, a ultimately all information that entrepreneurs decide to process have to lead them to make better calls. A dashboard does it perfectly through its emphasis on clutter-free data and use of statistical tools to decide that for this kind of business use, only such parameters are relevant. For instance, at the end of the financial year, businesses are interested in profit, earning per share and equity, so only these make it to a statistical dashboard.

What are types of data?

Broadly, data is either qualitative or quantitative. We have two further subdivisions of discrete or continuous data in numerical or quantitative data. Discrete data always has finite values. Continuous data, on the other hand, is data that can have many values from a given set of data points. E.g., temperature range. Qualitative data, further, has nominal data akin to data on a particular characteristic that people or things can be differentiated into at all times: Eg, hair color, geography where they live, etc. Ordinal data, however, rank people and things into categories based on attributes like height, wealth, etc.

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Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

• 1.1 AP Statistics Topics for Project
• 1.2 Statistics Project Topics for High School Students
• 1.3 Statistical Survey Topics
• 1.4 Statistical Experiment Ideas
• 1.5 Easy Stats Project Ideas
• 1.6 Business Ideas for Statistics Project
• 1.7 Socio-Economic Easy Statistics Project Ideas
• 1.8 Experiment Ideas for Statistics and Analysis
• 2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

• Challenging concepts suitable for advanced placement courses.
• Accessible ideas that are engaging and educational for younger students.
• Ideas for conducting surveys and analyzing the results.
• Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

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AP Statistics Topics for Project

• Analyzing Variance in Climate Data Over Decades.
• The Correlation Between Economic Indicators and Standard of Living.
• Statistical Analysis of Voter Behavior Patterns.
• Probability Models in Sports: Predicting Outcomes.
• The Effectiveness of Different Teaching Methods: A Statistical Study.
• Analysis of Demographic Data in Public Health.
• Time Series Analysis of Stock Market Trends.
• Investigating the Impact of Social Media on Academic Performance.
• Survival Analysis in Clinical Trial Data.
• Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

• The Mathematics of Personal Finance: Budgeting and Spending Habits.
• Analysis of Class Performance: Test Scores and Study Habits.
• A Statistical Comparison of Local Public Transportation Options.
• Survey on Dietary Habits and Physical Health Among Teenagers.
• Analyzing the Popularity of Various Music Genres in School.
• The Impact of Sleep on Academic Performance: A Statistical Approach.
• Statistical Study on the Use of Technology in Education.
• Comparing Athletic Performance Across Different Sports.
• Trends in Social Media Usage Among High School Students.
• The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

• Public Opinion on Environmental Conservation Efforts.
• Consumer Preferences in the Fast Food Industry.
• Attitudes Towards Online Learning vs. Traditional Classroom Learning.
• Survey on Workplace Satisfaction and Productivity.
• Public Health: Attitudes Towards Vaccination.
• Trends in Mobile Phone Usage and Preferences.
• Community Response to Local Government Policies.
• Consumer Behavior in Online vs. Offline Shopping.
• Perceptions of Public Safety and Law Enforcement.
• Social Media Influence on Political Opinions.

Statistical Experiment Ideas

• The Effect of Light on Plant Growth.
• Memory Retention: Visual vs. Auditory Information.
• Caffeine Consumption and Cognitive Performance.
• The Impact of Exercise on Stress Levels.
• Testing the Efficacy of Natural vs. Chemical Fertilizers.
• The Influence of Color on Mood and Perception.
• Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
• The Effectiveness of Different Types of Water Filters.
• Analyzing the Impact of Room Temperature on Concentration.
• Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

• Average Daily Screen Time Among Students.
• Analyzing the Most Common Birth Months.
• Favorite School Subjects Among Peers.
• Average Time Spent on Homework Weekly.
• Frequency of Public Transport Usage.
• Comparison of Pet Ownership in the Community.
• Favorite Types of Movies or TV Shows.
• Daily Water Consumption Habits.
• Common Breakfast Choices and Their Nutritional Value.
• Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

• Analyzing Customer Satisfaction in Retail Stores.
• Market Analysis of a New Product Launch.
• Employee Performance Metrics and Organizational Success.
• Sales Data Analysis for E-commerce Websites.
• Impact of Advertising on Consumer Buying Behavior.
• Analysis of Supply Chain Efficiency.
• Customer Loyalty and Retention Strategies.
• Trend Analysis in Social Media Marketing.
• Financial Risk Assessment in Investment Decisions.
• Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

• Income Inequality and Its Impact on Education.
• The Correlation Between Unemployment Rates and Crime Levels.
• Analyzing the Effects of Minimum Wage Changes.
• The Relationship Between Public Health Expenditure and Population Health.
• Demographic Analysis of Housing Affordability.
• The Impact of Immigration on Local Economies.
• Analysis of Gender Pay Gap in Different Industries.
• Statistical Study of Homelessness Causes and Solutions.
• Education Levels and Their Impact on Job Opportunities.
• Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

• Multivariate Analysis of Global Climate Change Data.
• Time-Series Analysis in Predicting Economic Recessions.
• Logistic Regression in Medical Outcome Prediction.
• Machine Learning Applications in Statistical Modeling.
• Network Analysis in Social Media Data.
• Bayesian Analysis of Scientific Research Data.
• The Use of Factor Analysis in Psychology Studies.
• Spatial Data Analysis in Geographic Information Systems (GIS).
• Predictive Analysis in Customer Relationship Management (CRM).
• Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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• Foveal atrophy in patients with active central serous chorioretinopathy at first presentation: characteristics and treatment outcomes
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• Ki Young Son 1 ,
• Seul Gi Lim 2 ,
• Sungsoon Hwang 2 ,
• Jaehwan Choi 3 ,
• http://orcid.org/0000-0002-1502-3155 Sang Jin Kim 2 ,
• http://orcid.org/0000-0003-2125-1231 Se Woong Kang 2
• 1 Department of Ophthalmology , Chungnam National University Sejong Hospital , Sejong , Korea (the Republic of)
• 2 Department of Ophthalmology , Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul , Korea (the Republic of)
• 3 Department of Ophthalmology , Kyung Hee University Medical Center, Kyung Hee University , Seoul , Korea (the Republic of)
• Correspondence to Dr Se Woong Kang, Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Suwon, Korea (the Republic of); kangsewoong{at}gmail.com

Background/aims This study aimed to investigate the clinical characteristics and treatment outcomes of patients with active central serous chorioretinopathy (CSC) and foveal atrophy.

Methods Patients diagnosed with active idiopathic CSC using multimodal imaging and followed up for at least 6 months were included. They were divided into two groups (foveal atrophy group vs foveal non-atrophy group) according to a cut-off central foveal thickness of 120 µm on baseline optical coherence tomography (OCT). Baseline characteristics, angiographic and tomographic features and treatment outcomes were compared between the two groups.

Results Of the 463 patients, 92 eyes of 92 patients (19.9%) were in the foveal atrophy group and 371 eyes of 371 patients (80.1%) were in the foveal non-atrophy group. The baseline subretinal fluid (SRF) height was 111.3±76.8 µm in the foveal atrophy group and 205.0±104.4 µm in the foveal non-atrophy group on OCT images (p<0.001). Complete resolution of SRF after treatment was noted in 60.4% and 93.5% of patients in the foveal atrophy and foveal non-atrophy groups at the final visit, respectively (p<0.001). The foveal atrophy group showed worse visual acuity at baseline (logarithm of the minimum angle of resolution, 0.43±0.33 vs 0.13±0.18, p<0.001) and final visit (0.41±0.32 vs 0.05±0.15, p=0.035).

Conclusions CSC with foveal atrophy was associated with a shallow SRF height, low treatment efficacy and poor vision before and after treatment. We suggest that early active treatment should be considered for eyes with CSC accompanied by a persistent shallow SRF and foveal atrophy.

Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study.

https://doi.org/10.1136/bjo-2023-324147

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Foveal atrophy has been known as one of the sight-threatening complications in chronic central serous chorioretinopathy (CSC). However, the incidence and clinical significance of foveal atrophy at first presentation in patients with CSC have not been documented yet.

WHAT THIS STUDY ADDS

CSC with foveal atrophy accounted for 20% of active CSC and was closely related to persistent shallow subretinal fluid. In these eyes, the treatment outcomes and visual prognosis were poor.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

In the clinical setting, the prompt and active treatment would be required for preventing visual loss in patients with CSC with prolonged symptom duration, which may indicate persistent shallow subretinal fluid, and foveal attenuation.

Introduction

Central serous chorioretinopathy (CSC) is a chorioretinal disease with complex aetiology characterised by serous elevation of the neurosensory retina and focal detachment of the sub-retinal pigment epithelium (RPE). 1 Although there is evidence that the disease is strongly associated with dysfunction of the RPE and choroidal complex, the pathogenesis of CSC remains unclear. 2 3 Furthermore, there is no consensus regarding the classification of CSC. 4 The two most widely known entities are acute and chronic forms of the disease. Acute CSC is characterised by serous retinal detachment with limited focal or multifocal RPE alterations, leakage through the RPE on fluorescein angiography and resolution within 3–4 months. Chronic CSC is characterised by persistent serous retinal detachment after 4–6 months and is accompanied by widespread tracks of RPE atrophy and irregular RPE detachment. There is no consensus regarding the specific duration distinguishing chronic CSC from acute CSC, with the threshold typically set between 4 and 6 months in most published reports. 1 These temporal criteria have made it difficult for clinicians to determine the appropriate timing for intervention. Even in the chronic form of CSC, the natural course and phenotype are heterogeneous and vary significantly between individuals.

Foveal atrophy is a potential sight-threatening macular complication in patients with CSC. 5 6 Prolonged symptom duration, which represents persistent subretinal fluid (SRF), is associated with foveal attenuation in patients with CSC. 6 Furthermore, previous studies have reported that attenuation of foveal thickness, including the outer nuclear layer and foveal photoreceptor layer thickness, is associated with poor visual outcomes after resolution of SRF. 5–8 However, the incidence of foveal atrophy at first presentation of active CSC and its distinct clinical features have not been elucidated by large-scale studies.

In this study, we investigated the incidence of foveal atrophy at the initial visit of patients with active CSC, identified its clinical features and treatment outcomes and elucidated its pathogenesis.

Study design and settings

This retrospective comparative study included patients with active CSC at their initial visit to the Department of Samsung Medical Center, Seoul, Korea between January 2012 and June 2021. The study followed all guidelines for experimental investigation in human subjects.

Subjects aged 18 years or older who were diagnosed with active CSC in at least one eye were identified from the medical records. Subjects were included in the study if neurosensory detachment, including the presence of subfoveal fluid, was documented by optical coherence tomography (OCT), and one or more sites of leakage at the level of the RPE were confirmed by fluorescein angiography at the first clinic visit. All participants were treatment naïve and completed a questionnaire during their first visit to the clinic. The questionnaire comprised seven short questions covering age, sex, duration of symptoms before an initial clinic visit, subjective visual symptoms, ophthalmic treatment history, systemic illness and sociobehavioural information. Eyes with other macular abnormalities, including idiopathic choroidal neovascularisation (CNV), high myopia, epiretinal membrane, neovascular age-related macular degeneration, severe glaucoma, or history of intraocular surgery, prior argon laser photocoagulation, photodynamic therapy, or intravitreal anti-vascular endothelial growth factor (VEGF) injection were excluded. Patients with intraretinal fluid in the fovea that interfered with foveal thickness measurements were also excluded. The subjects were divided into two groups: the foveal atrophy and foveal non-atrophy groups based on a central foveal thickness of 120 µm on spectral-domain OCT. The cut-off value of 120 µm was determined by a distribution < the mean −2 SD of the central foveal thickness (119 µm) in healthy volunteers reported in a previous study. 6 To confirm the cut-off value in this study, reference data were obtained from 57 eyes of 57 age-matched healthy volunteers aged 49.6±13.7 years (mean±SD; range: 23–78 years) with no history of eye disease and refractive errors less than ±6 dioptres. The central foveal thickness in healthy subjects was 180.7±18.7 µm (mean±SD; range: −2 SD to +2 SD=143.2–218.2), which did not exceed the established criterion of 120 µm for foveal atrophy. If both eyes were eligible as per the inclusion and exclusion criteria, the right eye was selected as the study eye.

Multimodal imaging and imaging analysis

All subjects underwent ocular examinations, including best corrected visual acuity (BCVA), manifest refraction, axial length measurement, applanation tonometry and slit-lamp biomicroscopy with a non-contact fundus lens (SuperField lens; Volk Optical, Mentor, Ohio, USA). Structural OCT imaging was performed using a spectral-domain OCT (Spectralis; Heidelberg Engineering, Heidelberg, Germany). The OCT images were obtained through horizontal and vertical crosshair scans and 6 mm radial scans at 30° intervals centred on the fovea. In addition, horizontal and vertical cross-sectional enhanced depth imaging OCT scans were obtained to evaluate choroidal features. A quality score greater than 25 was required for the OCT images to be included. Colour fundus photographs and autofluorescence images were obtained using a Topcon fundus camera (TRC-50Dx; Topcon Medical System, Oakland, New Jersey, USA). Confocal scanning laser ophthalmoscopy, fluorescein angiography and indocyanine green angiography images were obtained using an eye-tracked Spectralis Heidelberg retina angiograph plus OCT (Heidelberg Engineering), according to a standard imaging protocol. All OCT images were analysed by two independent and experienced graders (KYS and SGL), who were blinded to the patients’ information, using a computer-based calliper measurement tool in the spectral-domain OCT system. When the clinical data were ambiguous or when there were disagreements between the graders, a senior retinal specialist (SWK) confirmed the interpretation by further discussion.

Central foveal thickness was defined as the average distance between the internal limiting membrane and the outermost layer of the detached central fovea on the horizontal and vertical OCT images, with the steepest foveal excavation from the raster scans obtained at the first visit. We measured the distance between the internal limiting membrane and the tip of the outer segment at the fovea on OCT images with a uniform outer segment type. If there was an apical projection or a protruding outer segment among the uniform outer segments, we subtracted the distance between the layer of uniform outer segments and the tip of the protruded outer segments. The SRF height was measured as the maximum distance between the outer margin of the photoreceptor layer and the inner surface of the RPE layer on OCT obtained during the initial visit. The subfoveal choroidal thickness, diameter of the largest choroidal vessel, presence of flat irregular pigment epithelial detachment, serous pigment epithelial detachment, turbid SRF, intraretinal and subretinal hyper-reflective foci, presence of extrafoveal cystoid macular degeneration, disruption of the ellipsoid zone, presence of RPE atrophy and inner choroidal attenuation were evaluated on initial OCT images. The presence of RPE atrophy was defined as evidence of choroidal hypertransmission (at least 250 µm in diameter) associated with an RPE defect and thinning of the outer retina on OCT images at baseline. 9–11 In addition, initial multimodal imaging findings were collected, including depigmentation of the RPE, gravitational tract, pachydrusen, soft drusen, acquired vitelliform-like lesions on colour fundus photography and autofluorescence, choroidal hyperpermeability, the presence of hyperfluorescent spots and locations in the mid-late phase of indocyanine green angiography, delayed choroidal arterial filling in the early phase of indocyanine green angiography (one disc diameter to larger areas with a geographical pattern) 12 and the pattern of RPE leaks on fluorescein angiography. Choroidal hyperpermeability was classified into patch (one or two disc diameters) and diffuse (larger than three disc diameters) types based on the size of the hyperfluorescence with blurred contours observed during the mid-phase of indocyanine green angiography.

Treatments and outcomes

All patients were observed for 3 months if there was serous neurosensory detachment of less than one disc diameter in size without RPE atrophy. In other cases, active treatments such as half-fluence photodynamic therapy or argon laser photocoagulation were considered.

Half-fluence photodynamic therapy with verteporfin (Visudyne, Charleston, South Carolina, USA) was applied to the area of choroidal hyperpermeability demonstrated by indocyanine green angiography. If discrete focal leakage points were located at more than one disc diameter away from the foveal centre, argon laser photocoagulation (MC-500 multicolour Argon Laser Photocoagulator; Nidek, Tokyo, Japan) was applied to the leakage point on fluorescein angiography. The endpoint of laser photocoagulation was to create a greyish burn by laser parameters of 0.1 s exposure time, spot size of 100–200 µm and 100–200 mW power.

Adjuvant treatment, such as an oral carbonic anhydrase inhibitor (acetazolamide, 100 mg/day), was administered at the discretion of the physician. Anti-VEGF therapy (a single intravitreal injection of 1.25 mg bevacizumab) has been used off-label in limited cases who met the following criteria: (1) lack of response to initial treatments such as photodynamic therapy, argon laser photocoagulation or adjuvant treatment including oral carbonic anhydrase inhibitors; (2) in cases where exudative pachychoroid neovasculopathy was not completely ruled out; and (3) occurrence of CNV subsequent to photodynamic therapy or argon laser photocoagulation.

Treatment outcomes were evaluated in three categories based on vertical and horizontal OCT images obtained 3 months after treatment. Complete resolution was defined as the complete absence of SRF (anatomical success), partial resolution as a reduction of >30% in baseline SRF height and no response as a reduction of less than 30% or an increase in baseline SRF height.

Statistical analysis

All statistical analyses were performed using SPSS software (V.23.0; SPSS). Categorical variables were analysed using Fisher’s exact test, the χ 2 test or linear-by-linear association for proportions. All continuous variables are reported as mean±SD and were compared using the t-test or Mann-Whitney test. Statistical significance was set at p<0.05.

Data from 525 eyes of 525 patients with active CSC at initial visit were collected. A total of 62 eyes were excluded due to a follow-up period of less than 6 months (32 eyes), the presence of CNV (2 eyes), 13 high myopia (13 eyes) and an epiretinal membrane (15 eyes). A total of 463 eyes of 463 patients (74.1% males, 25.9% females) were included in the primary analysis. Among them, 92 eyes (19.9%) were in the foveal atrophy group and 371 eyes (80.1%) were in the foveal non-atrophy group ( figure 1 ).

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Flow chart of the study design. CNV, choroidal neovascularisation; CSC, central serous chorioretinopathy.

The baseline characteristics of the two groups are presented in table 1 . The mean ages of the foveal atrophy and foveal non-atrophy groups were 53.2±8.3 years (range=32–68) and 48.7±9.3 years (range=23–84), respectively (p<0.001). Male predominance was more pronounced in the foveal atrophy group. The foveal atrophy group included 79 men (85.9%) and the foveal non-atrophy group included 264 men (71.2%) (p=0.005). The prevalence and distribution of symptoms differed significantly between the groups. 79 patients (85.9%) in the foveal atrophy group had decreased visual acuity at baseline, followed by metamorphopsia or micropsia in five patients (5.4%) and relative central scotoma in five patients (5.4%). In the foveal non-atrophy group, on the other hand, 136 patients (36.9%) had decreased visual acuity, followed by 89 patients (24.1%) with relative central scotoma, 74 patients (20.1%) with metamorphopsia or micropsia and 59 patients (16.0%) with blurred vision (p<0.001). The symptom duration, defined as the interval between symptom onset and first visit to any ophthalmology clinic, was 39.8±55.4 months in the foveal atrophy group and 4.9±8.1 months in the foveal non-atrophy group (p<0.001). The baseline and final BCVA was worse in the foveal atrophy group than in the foveal non-atrophy group (logarithm of the minimum angle of resolution, 0.43±0.33 vs 0.13±0.18, p<0.001; 0.41±0.32 vs 0.05±0.15, p<0.001, respectively).

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Baseline characteristics of patients with active central serous chorioretinopathy according to presence of foveal atrophy

Baseline tomographic characteristics are presented in table 2 . The central foveal thickness was thinner in the foveal atrophy group (89.9±20.5 µm) than in the foveal non-atrophy group (162.3±24.8 µm) (p<0.001). The baseline SRF height at the central fovea was 111.3±76.8 µm (range: 10–432) in the foveal atrophy group and 205.0±104.4 µm (range: 41–498) in the foveal non-atrophy group (p<0.001). The prevalence of disrupted ellipsoid zones (86.8% vs 13.6%, p<0.001), RPE atrophy (80.4% vs 17.3%, p<0.001), flat irregular pigment epithelial detachment (40.7% vs 18.7%, p<0.001), turbid SRF (33.0% vs 11.7%, p<0.001) and cystoid macular degeneration (7.7% vs 1.1%, p=0.002) was higher in the foveal atrophy group (representative cases, figures 2 and 3 ).

Baseline spectral-domain optical coherence tomographic characteristics of patients with active central serous chorioretinopathy according to presence of foveal atrophy

Representative case of a male patient in mid-50s with central serous chorioretinopathy with foveal atrophy. (A) colour fundus photography image showing pigmentary abnormalities. (B) The late phase of a fluorescein angiography image showing diffuse and vague leakage. (C) The late phase of an indocyanine green angiography image showing scattered punctate hyperfluorescent spots. (D) The shallow subretinal fluid (SRF) and foveal attenuation are observed in an initial optical coherence tomography image (SRF height=68 µm, foveal thickness=63 µm).

Representative case of a male patient in mid-40s with central serous chorioretinopathy in the foveal non-atrophy group. (A) colour fundus photography image showing serous detachment. (B, C) The late phase of fluorescein angiography and indocyanine green angiography images showing a smokestack leakage pattern without punctate hyperfluorescent spots. (D) The serous elevation is observed in an initial optical coherence tomography image (subretinal fluid (SRF) height=359 µm, foveal thickness=183 µm).

Baseline fundoscopic and angiographic characteristics are presented in table 3 . On the baseline fluorescein angiographic images, a diffuse or vague pattern (57 eyes, 65.5%) was the most frequent pattern of RPE leakage in the foveal atrophy group. In contrast, the most frequent pattern of RPE leakage in the foveal non-atrophy group was the inkblot pattern (68.5%, p<0.001). On indocyanine green angiography, choroidal hyperpermeability was present in 56 eyes (65.1%) in the foveal atrophy group and in 176 eyes (48.3%) in the foveal non-atrophy group (p=0.006). The number of clusters of the hyperfluorescent spot was 2.59±2.05 in the foveal atrophy group and 2.12±1.88 in the foveal non-atrophy group (p=0.033). The total area of clusters of hyperfluorescent spots was wider in the foveal atrophy group (13.31±13.49 mm 2 ) than in the foveal non-atrophy group (5.91±7.91 mm 2 ) (p<0.001).

Baseline fundoscopic and angiographic characteristics of patients with active central serous chorioretinopathy according to presence of foveal atrophy

Treatment modalities and outcomes are presented in table 4 . There were no significant differences in the initial and secondary treatment modalities between the two groups. However, the responses to the initial and second treatments differed between the two groups. In the foveal non-atrophy group, 338 eyes (91.4%) showed complete resolution after the initial treatment, followed by 26 eyes (7.0%) with partial resolution, and 6 eyes (1.6%) with no response. In the foveal atrophy group, only 50 eyes (57.5%) showed complete resolution after the initial treatment, followed by 29 eyes (33.3%) with partial resolution, and 8 eyes (9.2%) with no response (p<0.001). Complete resolution of SRF was achieved in 60.4% of eyes with foveal atrophy and 93.5% of eyes without foveal atrophy (p<0.001) at the final visit. In the eyes treated with photodynamic therapy, the consecutive decline in the average SRF height after photodynamic therapy between the two groups is shown in figure 4 . The average SRF height before photodynamic therapy was 117.4±74.5 µm in the foveal atrophy group and 197.9±100.4 µm in the foveal non-atrophy group (Mann-Whitney test, p<0.001). After photodynamic therapy, the average SRF height initially declined, but it had plateaued with a shallow height in the foveal atrophy group (28.2±34.2 µm in 1 month, 23.8±36.4 µm in 3 months and 29.3±40.1 µm in 6 months). On the other hand, it continuously declined during 6 months in the foveal non-atrophy group (15.7±37.2 µm in 1 month, 7.6±32.0 µm in 3 months and 4.8±22.8 µm in 6 months).

Treatment and response of patients with active central serous chorioretinopathy according to presence of foveal atrophy

(A) The average subretinal fluid (SRF) height at baseline and 1, 3 and 6 months after photodynamic therapy (PDT) between the foveal atrophy (69 eyes) and foveal non-atrophy (289 eyes) groups. At baseline, the average SRF height in the foveal atrophy group was significantly lower than in the foveal non-atrophy group (117.4 µm vs 197.9 µm). After PDT, the average SRF height continuously declined over 6 months in the foveal non-atrophy group (15.7, 7.6 and 4.8 µm at 1, 3 and 6 months, respectively) with 219, 241 and 275 eyes showing complete resolution of SRF at the corresponding time points. However, the average SRF height initially declined and then plateaued in the foveal atrophy group (28.2, 23.8 and 29.3 µm in 29, 33 and 38 eyes showing complete resolution of SRF at 1, 3 and 6 months, respectively). (B) Changes in best corrected visual acuity (BCVA) at baseline and 6 months after PDT between two groups. Compared with foveal non-atrophy group, the eyes in the foveal atrophy group show less improvement in vision after PDT.

Because the shallow SRF height noted in the foveal atrophy group could be related to longer symptom duration, a subgroup analysis in the foveal atrophy group was performed for the SRF height according to symptom duration. In 17 eyes with a symptom duration of less than 10 months, the SRF height was 95.59±48.66 µm. In 13 eyes with a symptom duration of less than 5 months, the SRF height was 88.08±28.88 µm. Regardless of symptom duration, the SRF height in the foveal atrophy group was consistently shallow.

The 463 eyes of the 463 patients included in this study had clinical, tomographic and angiographic findings consistent with active CSC as well as varying foveal thickness and SRF height. Approximately 20% of eyes with active CSC had foveal atrophy at presentation. Piccolino et al reported that the incidence of an atrophic outer photoreceptor layer in patients with CSC was 50% (14/28 patients) at presentation. 5 In Wang et al ’s study, 37.5% of foveal atrophy was observed after resolution of SRF. 6 There is no consensus on the OCT definition of foveal atrophy among studies. Furthermore, the definition of foveal atrophy in this study (central foveal thickness less than 120 µm) is quite conservative compared with previous studies, and the incidence of foveal atrophy was apparently lower than those in previous reports. The current study, with a relatively large study population, indicated that one-fifth of the eyes with active CSC already had foveal atrophy at presentation, which was not an infrequent occurrence.

Remarkably, the foveal atrophy group had a shallower SRF than the foveal non-atrophy group at presentation. Reduced anterior displacement of the neurosensory retina may cause the eye to become less hyperopic and minimise symptoms such as blurred vision, relative central scotoma and metamorphopsia. In this study, the foveal atrophy group had a longer symptom duration (mean: 39.8 months vs 4.9 months) and a higher rate of non-specific visual symptoms like decreased visual acuity compared with the foveal non-atrophy group before the first visit to any ophthalmological clinic. We suppose that persistent shallow SRF might leave active CSC undetected for a long duration, resulting in foveal atrophy. Thus, persistent shallow SRF is the most important and distinct characteristic of CSC with foveal atrophy, and this clinical feature influences the visual prognosis of these patients. Additionally, a high frequency of hyper-reflective dots, diffuse RPE atrophy, subretinal turbid SRF and cystoid macular degeneration are associated with SRF chronicity. 1 14–16

We suppose that persistent shallow SRF may be the cause of longer symptom duration and the resultant poor visual outcomes in the foveal atrophy group. However, one may raise question whether longer symptom duration itself could be a cause of shallow SRF in the foveal atrophy group. This does not seem to be the case because the shallow SRF height was also noted in the subgroup of eyes with short symptom duration among the foveal atrophy group.

Angiographic features between the two groups revealed that the foveal atrophy group showed a higher proportion of choroidal hyperpermeability and delayed choroidal arterial filling than the foveal non-atrophy group. Foveal atrophy in CSC is closely related to the ischaemic condition of the choroidal inner layer leading to RPE damage, and underlying choroidal hyperpermeability is thought to be a functional consequence of choriocapillaris attenuation. 3 The area of hyperfluorescent spots, which is considered a characteristic of choroidal vascular hyperpermeability in CSC, 17 was widely distributed in the foveal atrophy group. This might represent chronically increased intrachoroidal hydrostatic pressure and diffuse extensive RPE damage. 18 On the other hand, the presence of these findings may provide clues to clinicians for the management of patients with CSC at high risk of developing foveal atrophy.

Interestingly, CSC with foveal atrophy showed low treatment efficacy, although there was no difference in the therapeutic strategy between the two groups. In particular, alterations in the SRF height following photodynamic therapy differed between the two groups. The SRF height was lower in the foveal atrophy group before photodynamic therapy. However, residual shallow SRF was more likely to persist in the foveal atrophy group 3 and 6 months after photodynamic therapy. Poor baseline BCVA, disruption of the ellipsoid zone, cystoid macular degeneration, absence of intense hyperfluorescent areas on indocyanine green angiography and diffuse hyperfluorescent patterns on indocyanine green angiography are factors associated with ineffective photodynamic therapy. 14 19–23 It is worth noting that these factors are also characteristic of CSC with foveal atrophy. Because of the self-limiting nature of CSC, 24 25 clinicians might overlook the significance of persistent shallow SRF in active CSC with foveal atrophy. This study suggests that CSC with shallow SRF and foveal atrophy may necessitate active treatment rather than conservative treatment or observation, considering the poor visual and treatment outcomes.

Furthermore, the current study suggests that active intervention should be considered in CSC with non-fluctuating persistent shallow SRF, even before the incidence of foveal atrophy, especially if there are multimodal imaging findings of chronic CSC, such as diffuse leakage and pigmented atrophy. Many of the cases in our study involved patients initially observed without specific intervention and were later referred to tertiary hospitals after deterioration. Therefore, the potential significance of this study lies in promoting vigilance in managing patients with CSC who present with persistent thin SRF, with or without foveal atrophy. To reiterate the main conclusion of the study, treatment should be initiated in the case of active CSC diagnosed on multimodal imaging and the presence of shallow subfoveal neurosensory detachment. In contrast, in the case of a diagnosis of active (or inactive) CSC based on multimodal imaging and the presence of ‘high’ neurosensory detachment, a wait-and-see policy may be considered. Future studies are required to ascertain the value of early active treatment for CSC with persistent shallow SRF.

This study had several limitations. First, it was retrospective and conducted at a single centre with a predominantly Asian population. Additionally, treatment strategies were not standardised in the present study. Therefore, despite the rarity of chronic CSC with shallow SRF and foveal atrophy, further longitudinal and large-scale studies are warranted. Second, because most patients were referred from primary clinics, the results do not represent the overall cases of this disease. Third, there is a possibility that the SRF height on OCT may not reflect the peak SRF amount during the episode of CSC. Fourth, we were unable to collect OCT angiography images in all cases because of the recent introduction of this imaging modality. Thus, the presence of pachychoroid neovasculopathy with SRF could not be ruled out in those cases. Fifth, because many variables were analysed between the two groups in this study, the probability of a type 1 error in the statistical results might have increased. However, by applying conservative adjustments for multiple testing and assuming 45 comparisons, the significant p values were <0.0011, as determined using the Bonferroni method for multiple testing (0.05 divided by 45). Despite these stringent adjustments, most variables retained statistical significance, thereby validating the conclusions of the study.

Nevertheless, we have demonstrated the incidence of foveal atrophy in a relatively large number of patients with CSC. This study confirmed a close relationship between persistent shallow SRF in CSC and the incidence of foveal atrophy. Furthermore, CSC and foveal atrophy at first presentation were associated with poor visual and treatment outcomes.

In conclusion, CSC with foveal atrophy accounted for 20% of the active CSC cases at first presentation and was closely associated with persistent shallow SRF. In these eyes, treatment outcomes and visual prognosis were poor. Prompt and active treatment is required to prevent visual loss in patients with CSC with persistent shallow SRF and foveal atrophy.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the Samsung Medical Center Ethics Committee (IRB file number: 2022-07-133) and adhered to the tenets of the Declaration of Helsinki (1964) and its later amendments. The board waived the requirement for informed consent due to the retrospective design of the study.

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Contributors KYS drafted and revised the manuscript and acquired and analysed the data. SH and JC revised the manuscript and analysed the data. SGL acquired and analysed the data. SWK designed, drafted, conceptualised, critically revised the manuscript and supervised the study. SJK designed and conceptualised the manuscript and critically revised it. SWK is the guarantor.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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