Category Education

🎥 Watch the Full Workshop Series

📌 Part 1: https://youtu.be/ozOfDbbs69o

📌 Part 2: https://youtu.be/MltYW2QKJOY

Introduction

In today’s data-driven world, effectively visualizing data is a critical skill for anyone presenting research, business reports, or scientific findings. However, poorly designed charts can mislead your audience, hide key insights, or create confusion. This guide, based on my two-part Better Data Visualization workshop, walks you through the best practices, common pitfalls to avoid, and effective techniques to create impactful data visualizations.

By the end of this article, you’ll know which chart types to use, when to use them, and how to optimize your visuals for clarity and accuracy.

🔹 Part 1: The Fundamentals of Data Visualization

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1️⃣ Why Data Visualization Matters

Raw numbers in a table are difficult to process visually. Our brains process images faster than text, so an effective visualization allows your audience to:

• ✅ Identify patterns and trends 🔍
• ✅ Compare different data points easily 📊
• ✅ Avoid misinterpretations 🚫
• ✅ Focus on key insights quickly 🎯

For example, consider Anscombe’s Quartet—a set of four datasets with identical statistical properties (mean, variance, correlation), but when plotted, they reveal very different distributions. This highlights why visualization is essential for accurate data interpretation.

2️⃣ The Core Principles of Visual Perception

Our brains interpret visual information based on gestalt principles, which include:

• ✔ Proximity – Elements close together are perceived as related
• ✔ Similarity – Elements with similar color, shape, or size form a group
• ✔ Enclosure – Boundaries create distinct visual groups
• ✔ Closure – Our brains fill in missing information in a pattern
• ✔ Continuity – The eye follows smooth paths
• ✔ Connection – Lines connecting elements imply relationships

Using these principles intentionally in your charts can enhance readability and insight clarity.

3️⃣ How to Direct Audience Attention in Your Charts

• ✔ Use color strategically – Highlight key data points with color contrast, but don’t overdo it.
• ✔ Remove distractions – Unnecessary grid lines, legends, and excessive data points add clutter.
• ✔ Use labels wisely – Directly label important values instead of using a separate legend.
• ✔ Limit the number of colors – Too many colors create confusion instead of clarity.

📌 Example: Instead of an overwhelming color-filled bar chart, gray out less important data and only highlight the key trend.

4️⃣ Avoid These Common Mistakes in Data Visualization

• ❌ Never use 3D charts – They distort perception and make accurate comparisons difficult.
• ❌ Avoid spaghetti charts – Too many overlapping lines create clutter. Use small multiples instead.
• ❌ Reduce legend dependency – Directly label lines and bars where possible.
• ❌ Use effective titles – A title should summarize the key insight of the chart, not just describe the data.
• ❌ Don’t use dual y-axes – It can mislead interpretation by making unrelated data appear correlated.

5️⃣ Pie Charts: Should You Ever Use Them?

• 📌 Pie charts are problematic because humans struggle to compare angles and areas accurately. A bar chart is almost always a better alternative.
• ✅ When to use a pie chart: Only when comparing two or three categories, with a clear dominant value (e.g., “90% of cases are caused by E. coli”).

🚀 Better Alternatives:

• ✔ Bar Charts – Easier to compare values directly
• ✔ Tree Maps – Useful for hierarchical proportions
• ✔ Donut Charts – Only when emphasizing a single category

🔹 Part 2: Advanced Data Visualization Techniques

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6️⃣ Visualizing Data Over Time

Best charts for time-series data:

• ✔ Line charts – The best for showing trends over time 📈
• ✔ Slope graphs – Great for comparing two time points 📊
• ✔ Sparklines – Small, inline charts for quick trend analysis 🔍

🚨 Common Pitfalls:

• ❌ Avoid using bar charts for time-series data – Line charts work better for trends.
• ❌ Avoid dual-axis line charts – They often mislead by forcing unrelated scales into comparison.

7️⃣ Showing Data Distribution

To analyze variability in data, use:

• ✔ Histograms – Show frequency distributions 📊
• ✔ Box plots (box-and-whisker plots) – Display median, quartiles, and outliers 📦
• ✔ Violin plots – Combine box plots with kernel density estimates 🎻
• ✔ Bee swarm plots – Show the exact distribution of individual data points 🐝
• 📌 Box plots are underutilized in research but extremely powerful for comparing multiple groups.

8️⃣ Visualizing Relationships Between Variables

For exploring correlations between two or more variables, use:

• ✔ Scatter plots – The go-to chart for relationship analysis 🔵
• ✔ Bubble charts – Add a third variable with bubble size 🔴
• ✔ Chord diagrams & Sankey diagrams – Show relationships between categories 🌐
• 🚨 Key Tip: Use color and size effectively in scatter plots to encode extra variables without overwhelming the audience.

9️⃣ Optimizing Data Tables for Readability

Not all data needs to be visualized—sometimes a well-designed table is the best option.

• 📌 Tips for effective tables:
• ✅ Bold headers and left-align text for readability
• ✅ Right-align numbers for easy comparison
• ✅ Reduce grid lines – Use subtle dividers instead
• ✅ Use color shading to guide the audience’s focus
• ✅ Add heatmaps or sparklines to highlight patterns
• 📌 Pro Tip: People naturally scan down a table—adjust spacing to guide them row by row, not column by column.

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🔹 Key Takeaways & Best Practices

• ✔ Prioritize clarity – Choose the simplest chart that conveys your message
• ✔ Highlight key insights – Use color, labels, and layout intentionally
• ✔ Avoid 3D effects – They distort perception and reduce accuracy
• ✔ Minimize clutter – Every element should serve a purpose
• ✔ Choose the right chart type – Match the visualization to the data type
• ✔ Be intentional with tables – Guide the reader’s focus with structure
• 🚀 Final Rule: Make your data easy to understand, not just easy to create.

📌 Watch the Full Workshop Series

📽 Part 1: Fundamentals of Data Visualization → https://youtu.be/ozOfDbbs69o

📽 Part 2: Advanced Techniques & Best Practices → https://youtu.be/MltYW2QKJOY

💬 What’s your favorite chart type? Have you spotted bad data visualization in research or media? Drop a comment below! 🚀

🔔 Subscribe for more insights on data visualization, research communication, and effective presentations!

Alireza FakhriRavari, PharmD, BCPS, BCIDP, AAHIVP is Department Chair and Associate Professor of Infectious Diseases at Loma Linda University School of Pharmacy.

In the world of data-driven decision-making, the ability to present data effectively has become a crucial skill. Whether you’re presenting to a board of directors, a team of researchers, or a group of students, how you communicate your data can make or break your message. This post will explore the key takeaways from the “Better Presentations 2024” lecture, providing insights into how to transform data-heavy slides into clear, engaging, and persuasive presentations.

The Problem: Complex Data, Confusing Visuals

Too often, presentations get bogged down in complexity. The challenge isn’t just about including all relevant data, but ensuring that the audience can understand and engage with that information quickly and intuitively. While it’s tempting to showcase all of the work behind your findings, cluttered and overly complex visuals can lead to confusion rather than clarity.

The solution? Simplifying data visuals, focusing on key messages, and selecting the right charts to communicate those messages effectively.

1. Visualize Relevant Data: The Power of Simplicity

One of the core principles from the lecture was the importance of visualizing only the most relevant data. Instead of cramming every available statistic onto a slide, focus on the data that supports your key takeaway. This approach ensures that the audience’s attention is directed where it needs to be.

For instance, consider the classic mistake of adding too many variables to a single chart. This might turn into what we call a “spaghetti chart,” where lines overlap and the audience loses track of what matters. A better strategy is to break the data into multiple simpler charts, each highlighting one aspect of the data story. This way, your audience can process information one step at a time without feeling overwhelmed.

Key takeaway: Each slide should answer a single question and deliver a clear, concise message.

2. Avoid 3D Charts: A Misleading Trap

3D charts, while visually striking, can distort data interpretation. They often exaggerate certain aspects of the data while minimizing others, creating an illusion that can mislead the audience. The lecture strongly recommended avoiding 3D charts altogether.

For example, a 3D pie chart may make some segments appear larger or smaller than they actually are, leading to incorrect conclusions. Instead, stick to flat, 2D visualizations that maintain the integrity of your data.

Key takeaway: Avoid 3D charts, which can distort your data. Stick to 2D visuals for clarity.

3. Reconsider Pie Charts: Limited Utility

While pie charts can be useful for showing parts of a whole, they often fall short when comparing multiple data points. The human eye struggles to differentiate between similar slice sizes, and pie charts become especially ineffective when there are more than four or five segments.

In most cases, a simple bar chart will communicate the same information more effectively. Bar charts allow for easier comparison between categories and are generally more readable.

Key takeaway: Use pie charts sparingly, and only when they clearly convey the message. Otherwise, opt for a bar chart.

4. Integrating Graphics and Text: A Cohesive Message

Another valuable lesson from the lecture was the importance of integrating graphics with text. Too often, presenters rely solely on visuals or, conversely, on text-heavy slides. The best presentations use a balance of both.

For example, placing text directly on or next to a chart helps the audience understand what the visual is representing without having to mentally bridge the gap between the chart and a separate text box. This makes the flow of information smoother and reduces cognitive load.

Key takeaway: Combine graphics and text to tell a cohesive story. Don’t leave your audience guessing what the chart represents.

5. Data Storytelling: Crafting a Narrative

Data on its own rarely tells a compelling story. It’s the presenter’s job to weave a narrative around the data. The lecture emphasized the importance of creating a storyline that ties together your data points, leading your audience through a logical sequence of ideas.

To do this effectively, start by identifying the core message of your presentation. What do you want your audience to walk away with? Then, build your presentation around that message, using each data point as a stepping stone in the narrative.

For example, rather than simply listing statistics about market growth, you could frame the data as a story of how the market has evolved over time, identifying key drivers and moments of change.

Key takeaway: Use data to support a narrative, not as a standalone element. Every data point should contribute to the overarching story.

6. Remove Clutter: The Zen of Data Visualization

Clutter is the enemy of clarity. The lecture advocated for removing all unnecessary elements from your slides, such as gridlines, excessive labels, and decorative elements that don’t add value to the message.

Minimalism in data presentation helps ensure that the audience focuses on the most important aspects of the chart. One method to achieve this is through the “data-ink ratio” principle, which encourages maximizing the amount of data ink on a slide while minimizing non-essential ink. In other words, make every visual element count.

For example, instead of showing every data point in a time series, you might highlight only the key moments of change, allowing the audience to focus on the trend rather than getting lost in the noise.

Key takeaway: Less is more. Remove any elements that don’t serve a purpose in telling your data story.

7. Color Choice and Accessibility: Be Intentional

Color can be a powerful tool in data visualization, but it must be used intentionally. The lecture stressed the importance of choosing colors that are not only visually appealing but also accessible to all audience members, including those with color blindness.

A good rule of thumb is to limit your color palette to two or three main colors, using them consistently throughout the presentation. Avoid overuse of bright or clashing colors, which can distract from the data.

In addition, make sure that your charts are legible in grayscale. This ensures that your visuals are accessible to those who are colorblind or those viewing your presentation in black and white.

Key takeaway: Use color purposefully, and ensure that your visuals are accessible to all viewers.

8. Know Your Audience: Tailoring Your Presentation

Perhaps one of the most important lessons from the lecture was understanding your audience. The complexity of your data and the level of detail you provide should be tailored to the knowledge level of your audience.

For example, if you’re presenting to a group of experts, you might dive deeper into technical details. But if your audience is made up of general stakeholders or clients, you’ll want to focus on high-level trends and key takeaways, avoiding unnecessary jargon or overly complex data points.

Key takeaway: Tailor your presentation to the needs and knowledge level of your audience.

Conclusion: Better Data, Better Presentations

In 2024, effective presentations are about more than just showing data. They’re about crafting a narrative, simplifying complex information, and delivering a clear message that resonates with your audience. By following the principles outlined in this lecture, you can transform your data-heavy presentations into powerful stories that not only inform but also inspire action.

Whether you’re presenting in a boardroom, classroom, or conference, these strategies will help ensure that your message is heard, understood, and remembered.

Key Takeaways:

1. Visualize only the most relevant data.
2. Avoid 3D charts and be cautious with pie charts.
3. Integrate graphics and text for a smoother flow of information.
4. Craft a narrative around your data.
5. Remove unnecessary clutter to enhance clarity.
6. Use color with intention and accessibility in mind.
7. Tailor your presentation to your audience’s needs.

By mastering these techniques, you can elevate your presentations, making data easier to understand and your message more impactful. The future of data visualization lies not in more complexity, but in the art of simplicity and clear communication.

References:

• Schwabish J. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press; 2021.
• Schwabish J. Better Presentations: A Guide for Scholars, Researchers, and Wonks. New York: Columbia University Press; 2017.

Alireza FakhriRavari, PharmD, BCPS, BCIDP, AAHIVP is Department Chair and Associate Professor of Infectious Diseases at Loma Linda University School of Pharmacy.

June 6, 2023

Artificial intelligence (AI) is rapidly transforming many aspects of our lives, from healthcare to transportation to entertainment. But what about education? How can AI be used to enhance teaching and learning, and what are the potential risks and benefits?

These are some of the questions addressed in a recent report from the U.S. Department of Education titled “Artificial Intelligence and the Future of Teaching and Learning“. The report provides an overview of how AI is being used in education today, as well as recommendations for how educators, policymakers, and technology providers can work together to maximize its benefits.

One potential benefit of AI in education is personalized learning experiences. By analyzing data on student performance and behavior, AI systems can provide tailored recommendations for each student’s individual needs. This could help students learn more efficiently and effectively, while also freeing up teachers’ time to focus on other tasks.

Another potential benefit is improved student outcomes. By providing real-time feedback on student progress, AI systems can help identify areas where students need additional support or challenge. This could lead to better academic performance overall, as well as increased engagement and motivation among students.

However, there are also concerns about the use of AI in education. One major concern is data privacy – if sensitive information about students is being collected by AI systems, it must be protected from unauthorized access or misuse. Another concern is bias in algorithms – if an AI system is trained on biased data or programmed with biased assumptions, it could perpetuate existing inequalities or stereotypes.

To address these concerns and maximize the benefits of AI in education, the report recommends a collaborative approach that involves all stakeholders working together to develop policies and practices that prioritize equity, transparency, and accountability. For example:

• Policymakers should invest in research on AI in education to better understand its potential impact.
• Educators should receive professional development opportunities to learn about AI and how to use it effectively.
• Technology providers should develop ethical guidelines for the use of AI in education, and ensure that their products are designed with equity and inclusion in mind.
• All stakeholders should work together to ensure that students have access to high-quality learning experiences regardless of their background or socioeconomic status.

Overall, the report emphasizes that while AI has great potential to transform teaching and learning, it must be implemented thoughtfully and with a focus on equity and inclusion. By working together, educators, policymakers, and technology providers can help ensure that AI is used in ways that benefit all students.

Source: https://www2.ed.gov/documents/ai-report/ai-report.pdf

Alireza FakhriRavari, PharmD, BCPS, BCIDP, AAHIVP is an Assistant Professor of Infectious Diseases at Loma Linda University School of Pharmacy.