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Data Presentation - Types & Its Importance

What is data presentation.

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

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

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

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

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

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

Methods of Data Presentation in Statistics

1. pictorial presentation.

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

2. Column chart

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

3. Pie Charts

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

4. Bar charts

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

5. Histograms

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

6. Box plots

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

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

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

Importance of Data Presentation

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

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

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

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

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

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

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

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

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Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro. We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

• Clarity : Data presentations make complex information clear and concise.
• Engagement : Visuals, such as charts and graphs, grab your audience's attention.
• Comprehension : Visual data is easier to understand than long, numerical reports.
• Decision-making : Well-presented data aids informed decision-making.
• Impact : It leaves a lasting impression on your audience.

Types of data presentation:

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

• Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
• Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
• Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
• Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation?

When creating your data presentation, remember these key components:

• Data points : Clearly state the data points you're presenting.
• Comparison : Highlight comparisons and trends in your data.
• Graphical methods : Choose the right chart or graph for your data.
• Infographics : Use visuals like infographics to make information more digestible.
• Numerical values : Include numerical values to support your visuals.
• Qualitative information : Explain the significance of the data.
• Source citation : Always cite your data sources.

How to structure an effective data presentation?

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

• Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
• High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
• Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
• Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

• Use visuals : Incorporate charts and graphs to enhance understanding.
• Keep it simple : Avoid clutter and complexity.
• Highlight key points : Emphasize crucial data.
• Engage the audience : Encourage questions and discussions.
• Practice : Rehearse your presentation.

Don'ts:

• Overload with data : Less is often more; don't overwhelm your audience.
• Fit Unrelated data : Stay on topic; don't include irrelevant information.
• Neglect the audience : Ensure your presentation suits your audience's level of expertise.
• Read word-for-word : Avoid reading directly from slides.
• Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

• Definition : Data presentation is the art of visualizing complex data for better understanding.
• Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
• Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
• Choosing methods : Select the right method based on data, audience, and purpose.
• Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
• Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
• Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

FAQ's on a data presentation

1. what is data presentation, and why is it important in 2024.

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

• Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
• On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
• Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
• Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
• Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive! Sign up for our free trial or book a demo ! ‍

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

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

Table of Contents

What is a Data Presentation?

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

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

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

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

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

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

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

Real-Life Application of Bar Charts

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

Step 1: Selecting Data

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

The sales manager has highlighted these products for the presentation.

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

Step 2: Choosing Orientation

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

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

Step 3: Colorful Insights

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

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

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

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

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

Real-life Application of Line Graphs

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

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

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

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

Step 3: Connecting Trends

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

Step 4: Adding Clarity with Color

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

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

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

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

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

Real-Life Application of a Dashboard

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

Step 1: Defining Key Metrics

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

Step 2: Choosing Visualization Widgets

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

Step 3: Dashboard Layout

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

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

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

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

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

Real-Life Application of a Treemap Chart

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

Step 1: Define Your Data Hierarchy

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

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

Step 2: Choose a Suitable Tool

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

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

Step 3: Make a Treemap Chart with PowerPoint

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

Step 5: Input Your Data

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

Step 6: Customize the Treemap

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

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

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

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

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

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

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

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

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

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

Real-Life Application of Pie Charts

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

Step 1: Define Your Data Structure

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

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

Step 2: Insert a Pie Chart

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

For instance:

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

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

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

Step 03: Results Interpretation

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

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

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

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

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

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

Real-Life Application of a Histogram

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

Step 1: Gather Data

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

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

Step 2: Define Bins

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

Step 3: Count Frequency

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

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

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

Step 4: Create the Histogram

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

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

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

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

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

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

Real-Life Application of Scatter Plot

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

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

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

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

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

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

1. Fact Sheet Dashboard for Data Presentation

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

Use This Template

2. 3D Column Chart Infographic PPT Template

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

3. Data Circles Infographic PowerPoint Template

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

4. Colorful Metrics Dashboard for Data Presentation

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

5. Animated Data Presentation Tools for PowerPoint & Google Slides

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

6. Statistics Waffle Charts PPT Template for Data Presentations

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

7. Data Presentation Dashboard Template for Google Slides

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

8. Weather Dashboard for Data Presentation

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

9. Social Media Marketing Dashboard Data Presentation Template

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

10. Project Management Summary Dashboard Template

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

11. Profit & Loss Dashboard for PowerPoint and Google Slides

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

Overwhelming visuals

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

Inappropriate chart types

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

Lack of context

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

Inconsistency in design

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

Failure to provide details

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

Lack of focus

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

Visual accessibility issues

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

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

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

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

If you need a quick method to create a data presentation, check out our  AI presentation maker . A tool in which you add the topic, curate the outline, select a design, and let AI do the work for you.

[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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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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4 Presenting Data

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Data presentation can greatly influence audiences. This chapter reviews principles and approaches for presenting data, focusing on whether data needs to be used. Data can presented using words alone (e.g., metaphors or narratives), numbers (e.g., tables), symbols (e.g., bar charts or line graphs), or some combination that integrates these methods. Although new software packages and advanced techniques are available, visual symbols that can most readily and effectively communicate public health data are pie charts, bar charts, line graphs, icons/icon arrays, visual scales, and maps. Perceptual cues, especially proximity, continuation, and closure, influence how people process information. Contextual cues help enhance meaning by providing sufficient context to help audiences better understand data. Effective data presentation depends upon articulating the purpose for communicating, understanding audiences and context, and developing storylines to be communicated, taking into account the need to present data ethically and in a manner easily understood.

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Data Collection, Presentation and Analysis

• First Online: 25 May 2023

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This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.

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Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

* E-mail: [email protected]

Affiliation Division of Nephrology & Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America

Affiliations Division of Nephrology & Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America, Department of Biostatistics, Medical Faculty, University of Belgrade, Belgrade, Serbia

Affiliation Division of Biomedical Statistic and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America

• Tracey L. Weissgerber, 
• Natasa M. Milic, 
• Stacey J. Winham, 
• Vesna D. Garovic

Published: April 22, 2015

• https://doi.org/10.1371/journal.pbio.1002128
• Reader Comments

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals ( n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Citation: Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4): e1002128. https://doi.org/10.1371/journal.pbio.1002128

Copyright: © 2015 Weissgerber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Funding: This project was supported by Award Number P-50 AG44170 (Project 1, VDG) from the National Institute on Aging ( http://www.nia.nih.gov/ ). TLW and SJW were supported by the Office of Research on Women's Health (Building Interdisciplinary Careers in Women’s Health award K12HD065987; http://orwh.od.nih.gov/ ). This publication was made possible by CTSA Grant Number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS; http://www.ncats.nih.gov/ ), a component of the National Institutes of Health (NIH; http://www.nih.gov/ ). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: ANOVA, analysis of variance; ARRIVE, Animal Research: Reporting of In Vivo Experiments; SD, standard deviation; SE, standard error

Introduction

Data presentation is the foundation of our collective scientific knowledge, as readers’ understanding of a dataset is generally limited to what the authors present in their publications. Figures are critically important because they often show the data that support key findings. However, studies of the Journal of the American Medical Association [ 1 ] and the British Medical Journal [ 2 ] provide compelling evidence that fundamental changes in the types of figures that scientists use are needed. Authors generally use figures to present summary statistics, instead of providing detailed information about the distribution of the data or showing the full data [ 1 , 2 ].

Bar graphs are designed for categorical variables; yet they are commonly used to present continuous data in laboratory research, animal studies, and human studies with small sample sizes. Bar and line graphs of continuous data are “visual tables” that typically show the mean and standard error (SE) or standard deviation (SD). This is problematic for three reasons. First, many different data distributions can lead to the same bar or line graph ( Fig 1 and Fig 2 ). The full data may suggest different conclusions from the summary statistics ( Fig 1 and Fig 2 ). Second, additional problems arise when bar graphs are used to show paired or nonindependent data ( Fig 2 ). Figures should ideally convey the design of the study. Bar graphs of paired data erroneously suggest that the groups being compared are independent and provide no information about whether changes are consistent across individuals (Panel A in Fig 2 ). Third, summarizing the data as mean and SE or SD often causes readers to wrongly infer that the data are normally distributed with no outliers. These statistics can distort data for small sample size studies, in which outliers are common and there is not enough data to assess the sample distribution.

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The full data may suggest different conclusions from the summary statistics. The means and SEs for the four example datasets shown in Panels B–E are all within 0.5 units of the means and SEs shown in the bar graph (Panel A). p -values were calculated in R (version 3.0.3) using an unpaired t-test, an unpaired t-test with Welch’s correction for unequal variances, or a Wilcoxon rank sum test. In Panel B, the distribution in both groups appears symmetric. Although the data suggest a small difference between groups, there is substantial overlap between groups. In Panel C, the apparent difference between groups is driven by an outlier. Panel D suggests a possible bimodal distribution. Additional data are needed to confirm that the distribution is bimodal and to determine whether this effect is explained by a covariate. In Panel E, the smaller range of values in group two may simply be due to the fact that there are only three observations. Additional data for group two would be needed to determine whether the groups are actually different.

https://doi.org/10.1371/journal.pbio.1002128.g001

The bar graph (mean ± SE) suggests that the groups are independent and provides no information about whether changes are consistent across individuals (Panel A). The scatterplots shown in the Panels B–D clearly demonstrate that the data are paired. Each scatterplot reveals very different patterns of change, even though the means and SEs differ by less than 0.3 units. The lower scatterplots showing the differences between measurements allow readers to quickly assess the direction, magnitude, and distribution of the changes. The solid lines show the median difference. In Panel B, values for every subject are higher in the second condition. In Panel C, there are no consistent differences between the two conditions. Panel D suggests that there may be distinct subgroups of “responders” and “nonresponders.”

https://doi.org/10.1371/journal.pbio.1002128.g002

In contrast, univariate scatterplots, box plots, and histograms allow readers to examine the data distribution. This approach enhances readers’ understanding of published data, while allowing readers to detect gross violations of any statistical assumptions. The increased flexibility of univariate scatterplots also allows authors to convey study design information. In small sample size studies, scatterplots can easily be modified to differentiate between datasets that include independent groups ( Fig 1 ) and those that include paired or matched data ( Fig 2 ).

We conducted a systematic review of standard practices for data presentation in scientific papers, contrasting the use of bar graphs versus figures that provide detailed information about the distribution of the data (scatterplots, box plots, and histograms). We focused on physiology because physiologists perform a wide range of studies, including human studies, animal studies, and in vitro laboratory experiments. We systematically reviewed all full-length, original research articles published in the top 25% of physiology journals between January 1 and March 31, 2014 ( n = 703) to assess the types of figures that were used to present continuous outcome data ( S1 Fig and Table A in S1 Text ). We also abstracted information on sample size and statistical analysis procedures, as these factors may influence figure selection. Detailed methods and results are presented in the data supplement. Based on our findings, we recommend major changes to standard practices for presenting continuous data in small sample size studies. We hope that these recommendations will promote scientific discourse by giving readers the information needed to fully examine published data.

Are Your Figures Worth a Thousand Words?

In addition to showing data for key findings, figures are important because they give authors the opportunity to display a large amount of data very quickly. However, most figures provided little more information than a table (Panel A in S2 Fig and S1 Text ). Bar graphs were the most commonly used figures for presenting continuous data. 85.6% of papers included at least one bar graph. Most of these papers used bar graphs that showed mean ± SE (77.6%, Panel B in S2 Fig ), rather than mean ± SD (15.3%). Line graphs and point and error bar plots were also common (61.3% of articles, Panel A in S2 Fig ), and most showed mean ± SE. Figures that provide detailed information about the distribution of the data were seldom used. 13.4% of articles included at least one univariate scatterplot, 5.3% included at least one box plot, and 8.0% included at least one histogram. The journals that we examined publish research conducted by investigators in many fields; therefore, it is likely that investigators in other disciplines follow similar practices. The overuse of bar graphs and other figures that do not provide information about the distribution of the data has also been documented in psychology [ 3 ] and medicine [ 1 , 4 ].

Our data show that most bar and line graphs present mean ± SE. Fig 3 illustrates that presenting the same data as mean ± SE, mean ± SD, or in a univariate scatterplot can leave the reader with very different impressions. While the scatterplot prompts the reader to critically evaluate the authors’ analysis and interpretation of the data, the bar graphs discourage the reader from thinking about these issues by masking distributional information. The question of whether investigators should report the SE or the SD has been extensively debated by biomedical scientists and statisticians [ 5 , 6 ]. We argue that figures for small sample size studies should show the full distribution of the data, rather than mean ± SE or mean ± SD. However, given that figures showing these summary statistics are ubiquitous in the biomedical literature, researchers should understand why the SE and SD can give such different visual impressions. The SD measures the variation in the sample, whereas the SE measures the accuracy of the mean. The SE is strongly dependent on sample size (SE = SD / √ n )—as sample size increases, the uncertainty surrounding the value of the mean decreases. If two samples have the same SE, the one with the larger sample size will have the larger SD. Showing the SE rather than the SD magnifies the apparent visual differences between groups. This effect is exacerbated when the groups being compared have different sample sizes, which is common in physiology and in other disciplines.

While scatterplots prompt the reader to critically evaluate the statistical tests and the authors’ interpretation of the data, bar graphs discourage the reader from thinking about these issues. Placental endothelin 1 ( EDN1 ) mRNA data for four different groups of participants is presented in bar graphs showing mean ± SE (Panel A), or mean ± SD (Panel B), and in a univariate scatterplot (Panel C). Panel A (mean ± SE) suggests that the second group has higher values than the remaining groups; however, Panel B (mean ± SD) reveals that there is considerable overlap between groups. Showing SE rather than SD magnifies the apparent visual differences between groups, and this is exacerbated by the fact that SE obscures any effect of unequal sample size. The scatterplot (Panel C) clearly shows that the sample sizes are small, group one has a much larger variance than the other groups, and there is an outlier in group three. These problems are not apparent in the bar graphs shown in Panels A and B.

https://doi.org/10.1371/journal.pbio.1002128.g003

The infrequent use of univariate scatterplots, boxplots, and histograms is a missed opportunity. The ability to independently evaluate the work of other scientists is a pillar of the scientific method. These figures facilitate this process by immediately conveying key information needed to understand the authors’ statistical analyses and interpretation of the data. This promotes critical thinking and discussion, enhances the readers’ understanding of the data, and makes the reader an active partner in the scientific process. In contrast, bar and line graphs are “visual tables” that transform the reader from an active participant into a passive consumer of statistical information. Without the opportunity for independent appraisal, the reader must rely on the authors’ statistical analyses and interpretation of the data.

Summary Statistics Are Only Meaningful When There Are Enough Data to Summarize

Sample size is an important consideration when designing figures and selecting statistical analysis procedures ( Box 1 ) for continuous data. Our analysis shows that most studies had very small sample sizes (Panel C in S2 Fig ). The minimum sample size for any group shown in a figure was four (median number of independent observations), with an interquartile range of three independent observations (25th percentile: n = 3, 75th percentile: n = 6). The maximum sample size for any group shown in a figure was ten, with an interquartile range of nine (25th percentile: n = 6, 75th percentile: n = 15). Univariate scatterplots would be the best choice for many of these small studies. The summary statistics shown in bar graphs, line graphs, and box plots are only meaningful when there are enough data to summarize. Histograms are difficult to interpret when there aren’t enough observations to clearly show the distribution of the data.

Box 1. Data Analysis

• The distribution of the data and the sample size are critical considerations when selecting statistical tests. Univariate scatterplots immediately convey this important information.
• T-tests and analysis of variance (ANOVA) are examples of parametric tests. These tests compare means and assume that the data are normally distributed with no outliers. In small samples, these tests are prone to errors if the data contain outliers or are not normally distributed.
• The Wilcoxon rank sum test is an example of a nonparametric test. Nonparametric tests don’t make assumptions about the distribution of the variables that are being assessed. These tests often compare the ranks of the observations or the medians across groups. Nonparametric statistics are often preferred to parametric tests when the sample size is small and the data are skewed or contain outliers.
• Some statisticians recommend nonparametric tests for small sample size studies. Others argue that these tests are underpowered, especially if the data distribution appears symmetric.
• Our data suggest that most authors assume that their data are normally distributed, use parametric statistical analysis techniques, and select figures that show parametric summary statistics (Table B in S1 Text ). 78.1% of studies performed only parametric analyses. 13.6% of studies used both parametric and nonparametric analyses, whereas 3.8% included only nonparametric analyses.
• More than half of the authors who performed non-parametric analyses showed means when presenting their data. Investigators should show medians whenever they use nonparametric statistical tests. Medians are often used in situations where the mean is misleading due to outliers or a skewed distribution.
• Investigators who use nonparametric statistics for paired or matched data should report the median difference instead of the median values for each condition ( Fig 2 ). Unlike means, medians are not additive. The median difference is not the same as the difference between the medians for each condition.
• Scientists and statisticians continue to debate many statistical practices that are commonly used in basic science research. These include whether to test the assumptions underlying parametric analyses [ 7 ], when to use parametric versus nonparametric tests [ 8 , 9 , 10 ], whether to report SD versus SE for normally distributed data [ 5 , 6 , 8 ], and how to use p -values [ 11 ]. The data presentation practices that we recommend will benefit scientists and statisticians on all sides of these debates by allowing others to examine the potential impact of using different statistical techniques.

Recommendations for a New Data Presentation Paradigm

These results suggest that, as scientists, we urgently need to change our standard practices for presenting and analyzing continuous data in small sample size studies. We recommend three changes to resolve the problems identified in this systematic review.

• Encourage a more complete presentation of data . We encourage investigators to consider the characteristics of their datasets, rather than relying on standard practices in the field, whenever they present data. The best option for small datasets is to show the full data, as summary statistics are only meaningful if there are enough data to summarize. In 75% of the papers that we reviewed, the minimum sample size for any group shown in a figure was between two and six. Univariate scatterplots are the best choice for showing the distribution of the data in these small samples, as boxplots and histograms would be difficult to interpret. By displaying the full dataset, scatterplots allow readers to detect gross violations of statistical assumptions and to determine whether the results would have been different using alternative statistical analysis techniques. This is especially important for investigators who use parametric analyses to compare groups in small studies. While Microsoft Excel allows scientists to quickly and efficiently create bar graphs, univariate scatterplots are more challenging. We created free Excel templates that are available in the supplemental files for the manuscript ( S2 Text and S3 Text ). The templates can also be downloaded from CTSpedia ( https://www.ctspedia.org/do/view/CTSpedia/TemplateTesting ), where we will post updated versions. Researchers can quickly enter data to make univariate scatterplots for paired data, independent data, and independent data with points jittered so that points with similar values do not overlap. The supplemental files also include detailed instructions for investigators who wish to make univariate scatterplots for paired or independent data using Graph Pad PRISM ( S4 Text , S5 Text and S6 Text ).
• Change journal policies . We strongly recommend that journals change their editorial policies and peer review practices to discourage the use of bar graphs and encourage the use of univariate scatterplots, boxplots, and histograms to present continuous data. Journal policies should provide specific guidance about what types of figures are preferred. Nonspecific policies stating that figures are preferred to tables whenever possible do not effectively promote the use of figures that show the distribution of continuous data (Table C in S1 Text , Table D in S1 Text , and S1 Text ). Journals play a crucial role in redefining standard practices in scientific research [ 12 ]. However, editorial policies are only effective if they are implemented. There were few improvements in scientific reporting among animal studies two years after the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were published, despite endorsement by top journals and funding agencies [ 13 ]. These guidelines were designed to encourage reporting of key methodological details in animal studies. Journals seeking to implement the policy changes recommended in this paper will need to work with editors and reviewers [ 14 ] to accomplish this goal.
• Train investigators in data presentation . This systematic review demonstrates that scientists need better training in how to select the appropriate type of figure for their data. A visually appealing figure is of little value if it is not suitable for the type of data being presented. Investigators should consider the type of outcome variable (categorical versus continuous), the sample size and the study design (independent versus nonindependent data, etc.) when designing figures. Presenting data in scientific publications is a critical skill for scientists [ 15 ], although this information is not universally included in statistics courses. This systematic review demonstrates that most scientists who publish in top physiology journals work with very small datasets. However, in the authors’ experience, statistics courses in many basic science departments are taught by statisticians, epidemiologists, or other researchers who perform complex analyses in very large datasets. Effective statistics instruction cannot follow a “one size fits all” approach [ 15 ]. Statistics instructors need to consider the types of data that their students will be working with and the standard practices in their students’ fields when designing courses. Basic science departments should work with instructors to develop course materials that will address the needs of their students and faculty. Data presentation training should include techniques for small sample size studies and address the problems with the standard practices identified in this review.

Conclusions

Our systematic review identified several critical problems with the presentation of continuous data in small sample size studies. A coordinated effort among investigators, medical journals, and statistics instructors is recommended to address these problems. We created free Excel templates ( S2 Text and S3 Text , https://www.ctspedia.org/do/view/CTSpedia/TemplateTesting ) that will allow researchers to quickly make univariate scatterplots for independent data (with or without overlapping points) and nonindependent data. We hope that improved data presentation practices will enhance authors’, reviewers’, and readers’ understanding of published data by ensuring that publications include the information needed to critically evaluate continuous data in small sample size studies.

Supporting Information

S1 text. supplemental methods and results..

This file contains the methods and results for the systematic review, including Table A in S1 Text, Table B in S1 Text, Table C in S1 Text and Table D in S1 Text. Table A in S1 Text: The number of articles examined by journal. Values are n, or n (% of articles reviewed that were eligible and included in the analysis). Journals are organized by 2012 impact factor. Articles that were not full length original research articles were excluded after screening (i.e. reviews, editorials, perspectives, commentaries, letters to the editor, short communications, etc.). Abbreviations: AJP, American Journal of Physiology; APS, American Physiological Society. *APS Journal. Table B in S1 Text: Most studies performed parametric analyses. Values are n (%). *n (%) of 493 articles which performed parametric analyses. The remaining articles did not specifically state whether these assumptions were tested. Table C in S1 Text: Relationship between journal affiliation and the use of bar graphs and univariate scatterplots. Abbreviations: APS, American Physiological Society. Seven of the top 20 physiology journals are published by the American Physiological Society (APS), which specifies that outcome data should be presented in figures rather than in tables whenever possible. Nonhuman studies did not include human participants, tissues, cells or cell lines. Human studies included human participants, tissues, cells or cell lines. Table D in S1 Text: Relationship between journal affiliation and the use of histograms and line graphs/point and error bars plots. Abbreviations: APS, American Physiological Society. Seven of the top 20 physiology journals are published by the American Physiological Society (APS), which specifies that outcome data should be presented in figures rather than in tables whenever possible.

https://doi.org/10.1371/journal.pbio.1002128.s001

S2 Text. Excel templates for creating univariate scatterplots for independent data.

Use this template to create scatterplots for independent data in two to five groups. Independent data means that the variable of interest is measured one time in each subject, and subjects are not related to each other. If your data do not meet these criteria, see the spreadsheet for paired or nonindependent data.

https://doi.org/10.1371/journal.pbio.1002128.s002

S3 Text. Excel templates for creating univariate scatterplots for paired or matched data.

Use this template to create scatterplots for paired or matched data. Paired data are when you measure the variable of interest more than one time in each participant. Matched data are when participants in groups one and two are matched for important characteristics. If your data are independent, please see the template for independent data. The template will allow you to create scatterplots for one group with two conditions, or two groups with two conditions.

https://doi.org/10.1371/journal.pbio.1002128.s003

S4 Text. Instructions for creating univariate scatterplots for independent data in GraphPad PRISM.

Use these instructions to create univariate scatterplots for independent data in one or more groups of subject using GraphPad PRISM 6.0. Independent data means that the variable of interest is measured one time in each participant or specimen and participants or specimens are not related to each other. If your data are paired or matched, please see the instructions for paired or matched data.

https://doi.org/10.1371/journal.pbio.1002128.s004

S5 Text. Instructions for creating univariate scatterplots for paired or matched data in GraphPad PRISM (one group, two conditions).

Use these instructions to create univariate scatterplots for paired or matched data (two or more conditions) in one group of participants or specimens using GraphPad PRISM 6.0. Paired data are when you measure the variable of interest more than one time in each participant. Matched data are when participants in group one and group two are matched for important characteristics. If your data are independent, please see the instructions for independent data.

https://doi.org/10.1371/journal.pbio.1002128.s005

S6 Text. Instructions for creating univariate scatterplots for paired or matched data in GraphPad PRISM (two groups, two conditions).

Use these instructions to create scatterplots for paired data (two conditions) in two groups of participants or specimens using GraphPad PRISM 6.0. Paired data are when you measure the variable of interest more than one time in each participant. If your data are independent, please see the instructions for Independent data.

https://doi.org/10.1371/journal.pbio.1002128.s006

S1 Fig. Study flow chart.

https://doi.org/10.1371/journal.pbio.1002128.s007

S2 Fig. Figure types, sample sizes, and statistical analysis.

Panel a: Bar graphs and other figures that typically show mean and SE or mean and SD were strongly preferred to figures that provide detailed information about the distribution of the data (scatterplots, box plots, and histograms). Panel b: Most bar graphs show mean ± SE. Panel c: Box plots show the minimum and maximum sample sizes for any group presented in a figure. The box shows the median and interquartile range. Whiskers show the furthest point that is within 1.5 times the interquartile range. Note that a few very high outliers are not shown ( n = 8 for minimum sample size; n = 7 for maximum sample size). The maximum values for minimum and maximum sample size per group were 593 and 2,192, respectively. Showing these outliers would make the box plots impossible to see. Seventeen studies were excluded from this analysis as sample size was not reported ( n = 614). Panel d: The types of figures that are selected depend on the type of statistical analysis that is performed. We performed ordinal logistic regression, with analysis type and figure type both classified as ordinal variables. The distribution of figure types differed significantly between studies that performed only parametric analyses and studies that performed both parametric and nonparametric analyses ( p < 0.001), and between studies that performed both types of analyses and studies that performed only nonparametric analyses ( p < 0.001).

https://doi.org/10.1371/journal.pbio.1002128.s008

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Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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Data Presentation in Qualitative Research: The Outcomes of the Pattern of Ideas with the Raw Data

• International Journal of Qualitative Research 1(3):196-200
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Presenting Research Data Effectively Through Tables and Figures

Presenting research data and key findings in an organized, visually attractive, and meaningful manner is a key part of a good research paper. This is particularly important in instances where complex data and information, which cannot be easily communicated through text alone, need to be presented engagingly. The best way to do this is through the use of tables and figures. They help to organize and summarize large amounts of data and present it in an easy-to-understand way.  

Tables are used to present numerical data, while figures are used to display non-numerical data, such as graphs, charts, and diagrams. There are different types of tables and figures, and choosing the appropriate format is essential to present the data effectively. This article provides some insights on how to present research data and findings using tables and figures.  

How to present research data in tables?

When complex data and statistical findings are too unwieldy or difficult to present either in text form or as figures, they can be presented through tables. Tables are best used where exact numerical values need to be analyzed and shared. It also aids in the comparison and contrast of various features or values among the different units. This allows swift and easy identification of patterns in the datasets. While presenting tables in a research paper, it is essential to incorporate certain core elements to ensure that readers are able to draw inferences and conclusions easily and quickly.  

• Title of the table :  The title should be concise and clear and communicate the purpose of the table. Tables must be referenced in the text through table numbers. Both the table number and the title are ideally mentioned just above the table. 
• Body of the table:  A crucial element in preparing the body of a table is to ensure uniformity in terms of units of measurement and the accurate use of decimal places. It is also important to format the table and ensure equal spacing between rows and columns.  
• Keep it simple and accurate:  It is important to ensure that only relevant information is presented in the table. One needs to be cautious not to populate tables with unnecessary information or design elements. Using plain fonts, in italics or bold, and the use of color or border styles help make the table visually appealing. Rows and columns must be labeled clearly and accurately to ensure that there is no ambiguity in analyzing the data presented. 

How to present research data in figures?

Figures are a powerful tool for visually presenting research data and key study findings. Figures are usually used to communicate trends or relationships and general patterns emerging from datasets. They are also used to present research data and complex information in a simpler form. Figures can take various forms like graphs, pie charts, scatter plots, line diagrams, drawings, maps, and photos. Early career researchers need to know how best to present figures in their research papers. The following are some core elements that should be incorporated.  

• Title:  Every figure must have a title that is clear and concise and must summarize the main point of the data being presented. It should be placed just below the figure. The numbering of the figures should be sequential and must correspond to the reference provided in the text. 
• Type of figure:  The type of figure to be used is usually dictated by the kind of information to be conveyed. Researchers need to decide which type of figure will enable readers to understand the information being shared easily. For example, scatter plots can be used to show relationships between two variables, pie charts can be used to illustrate relative proportions, and graphs can be used for the quantitative relationship between variables.  
• Use of Images:  When using figures, care should be taken to ensure that images are of a high resolution – sharp and clear. 
• Labeling:  Ensuring that all parts of the figures and the axes are labeled accurately is crucial if readers are to glean important details quickly. Use standard font sizes and styles. Experts also suggest the inclusion of scale bars in maps. 

Tips for Effectively Presenting Research Data through Tables and Figures

When presenting research data through tables or figures, it’s important to ensure that it is adding value to the text and not merely repeating values. This means taking care of certain vital aspects to ensure that the presentation is uniform, clear, and easy to read. Here are some tips to help you achieve that:

• Make sure that tables or figures add value to the text
• Ensure uniformity in numbering of tables, figures, and values both in the text and in the visual presentation
• Cite the source if tables and figures are used from a different source
• Use appropriate scales when creating tables and figures
• Use logarithmic scales if the data covers a wide range
• Use linear scales if the data is relatively small
• Check publication or style guide instructions of the target journal regarding the presentation of research data and findings, image resolution, presentation style, formatting, and so on
• Remember, tables and figures are only tools to convey information – using too many of them can overwhelm readers

In summary, presenting research data through tables and figures can be an effective way to convey information. However, it’s important to follow these tips to ensure that the presentation is clear and easy to read. By taking care of these vital aspects, researchers can effectively communicate their findings to their intended audience.

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• 6 Tips for Post-Doc Researchers to Take Their Career to the Next Level
• 5 Reasons for Rejection After Peer Review
• Ethical Research Practices For Research with Human Subjects
• Ethics in Science: Importance, Principles & Guidelines 

Empirical Research: A Comprehensive Guide for Academics 

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When and how should you use data in a presentation?

The answer is that you should use figures and numbers whenever they give the best evidence to back up your argument, or to tell your story. But how to present that data is more difficult.

Many people are not interested in tables of numbers, and may struggle to understand graphs. How can you help walk them through the data?

This page is designed to help you to answer that question by setting out some simple rules for presenting data.

Remember that You Are Telling Your Audience a Story

All presentations are basically story-telling opportunities.

Human beings have been hard-wired, over millions of years of evolution, to enjoy and respond to stories. It’s best to work with it, not fight it, because if you tell your audience a story, they are likely to listen much more carefully, and also move towards a logical conclusion: the insight to which you are trying to lead them.

Once you understand this, the issue of using data falls into place: it is to provide evidence of how your story unfolds.

Use Data to Tell the Story

You are not presenting data as such, you are using data to help you to tell your story in a more meaningful way.

This means that whenever you are required to present data, you should be asking yourself:

‘ What is the story in this data? ’,

‘ How best can I tell this story to my audience? ’

A Picture Tells a Thousand Words

90% of the information sent to the brain is visual and over 90% of all human communication is visual. Processing text requires our brains to work much harder than when processing images. In fact, the brain can process pictorial information 60,000 times faster than written information.

There is considerable truth in the saying ‘a picture tells a thousand words’ . It may not be literally a thousand, but it is often much easier to use a picture than to describe numerical information in words.

The data itself may be vitally important, but without a visual presentation of that data, its impact (and therefore your message) may be lost.

There are many people in the world who do not find it easy to understand numbers.

There are also many people who will simply switch off if you show them figures in a table. But if you present data in a graph or pie chart, you make a pictorial representation of the data. It makes the numbers much easier to understand. Trends and proportions become more obvious.

Consider this set of data:

Even for the highly numerate, the immediate point is only that there are lot more sales in the first quarter. You would have to do some adding up and dividing to work out the relationships between the four numbers. It also requires much more concentration to read and absorb the information in this format.

Now consider the same data in a pie chart:

It is immediately and shiningly obvious, even for those who struggle with numbers, that more than half of all sales were in the first quarter, and that over 75% were in the first two quarters.

What’s more, nobody is going to be straining from the back of the room to read your figures. You really can see a lot more from a picture.

But, and this is important, make sure that the graph is a good one.

Check that your graph or chart is visually appealing, that all the labels are clear, and that you have used an appropriate type of graph or chart. Poor graph-making is always obvious and can lead to confusion. Your message will also have much more impact if you choose the right type of graph or chart.

For more about this, see our page on Graphs and Charts .

KISS: Keep It Simple, Stupid!

When you’re good at statistics, it’s very tempting to do some really whizzy analysis. And once you’ve done that, you really want to show everyone how clever you are, and how much work you’ve done.

But does it really help to make your point?

Then don’t present it.

In the (relatively rare) cases when you actually need some really whizzy analysis, you then need to ask yourself whether everyone will understand it. And, in these days of presentations being posted on the internet, will the casual reader of your slides understand it later?

Once again, if the answer is ‘probably not’, then don’t use it.

Leave It Out...

If you can’t summarise your analysis in one or two brief and clear sentences, then don’t include it.

It also follows that if you don’t need to include data to make your point, then it may be best not to do so. A slide that is likely to be misunderstood or produce confusion is worse than no slide at all. So cut out all unnecessary data and focus on what you really need  to tell your story .

Remember KISS: Keep It Simple, Stupid.

Highlight the Main Features to Draw Out the Insights

We’re not suggesting that you should ‘ dumb down ’ your presentation, but there is no harm in highlighting the key features, as well as cutting out unnecessary data.

Suppose once again that you are using the sales figures from the last four quarters. You want to show the actual figures. Why not use a highlighting tool to emphasise that the first quarter is more than half?

With PowerPoint and other presentation software, you can make each circle appear separately, as you make your point and discuss the insights.

A little creative use of the technology can help you to highlight certain figures, and once again, make the story clearer.

Take-home message

Paradoxically, your presentation of any data should be designed to move the conversation away from the data and into the insight and action that should result from it.

In other words:

‘What happened there?’

‘What are we going to do about it?’

If you look at your presentation, data and all, and it’s not clear how you would get from the data to the insight and then the action, it’s probably a good idea to look at it again.

Remember, it’s the story that matters… and then what happens as a result.

Continue to: Writing Your Presentation Working with Visual Aids

See also: What is Your Story? How to Identify Your Story from Raw Data Crisis Communications Presenting to Large Groups Simple Statistical Analysis

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
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Qualitative Research in General

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Social Constructivism

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Collecting Qualitative Data

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Constructivist Grounded Theory

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