Using Visuals to Enhance Scientific Presentations

10 data visualization mistakes that confuse your audience (and what to do instead) Poor chart choices can distort meaning and reduce trust, even when your analysis is correct. (Save this!) 𝟏. 𝐔𝐬𝐢𝐧𝐠 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭𝐬 𝐟𝐨𝐫 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐞𝐬 ↳ More than 5 slices become hard to read ↳ Pie charts work best for showing simple parts of a whole → Use bar charts when comparing many categories 𝟐. 𝐌𝐢𝐬𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐘-𝐀𝐱𝐢𝐬 𝐒𝐜𝐚𝐥𝐞𝐬 ↳ Non-zero baselines exaggerate differences ↳ Can unintentionally mislead viewers → Start bar charts at zero or clearly indicate axis breaks 𝟑. 𝐑𝐚𝐢𝐧𝐛𝐨𝐰 𝐂𝐨𝐥𝐨𝐫 𝐒𝐜𝐡𝐞𝐦𝐞𝐬 ↳ Too many colors create visual noise ↳ Colors lose meaning without intention → Use 3–5 purposeful colors to highlight insights 𝟒. 𝟑𝐃 𝐂𝐡𝐚𝐫𝐭𝐬 𝐓𝐡𝐚𝐭 𝐃𝐢𝐬𝐭𝐨𝐫𝐭 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 ↳ Perspective makes comparisons inaccurate ↳ Especially problematic in pie charts → Stick to clean 2D visualizations 𝟓. 𝐖𝐫𝐨𝐧𝐠 𝐂𝐡𝐚𝐫𝐭 𝐓𝐲𝐩𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 ↳ Line charts for categories or bars for trends cause confusion → Line for trends over time → Bar for category comparisons 𝟔. 𝐓𝐨𝐨 𝐌𝐚𝐧𝐲 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐨𝐧 𝐎𝐧𝐞 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 ↳ Information overload reduces clarity ↳ Viewers don't know where to focus → Highlight 3–5 key metrics that tell a story 𝟕. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐂𝐨𝐥𝐨𝐫𝐛𝐥𝐢𝐧𝐝 𝐀𝐜𝐜𝐞𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 ↳ Red–green combinations exclude many users → Use accessible palettes (blue–orange) plus labels or patterns 𝟖. 𝐂𝐡𝐚𝐫𝐭 𝐉𝐮𝐧𝐤 & 𝐔𝐧𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐃𝐞𝐜𝐨𝐫𝐚𝐭𝐢𝐨𝐧𝐬 ↳ Shadows, gradients, borders, and clip art distract from insights → Remove anything that doesn't add informational value 𝟗. 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐧𝐝 𝐋𝐚𝐛𝐞𝐥𝐬 ↳ Charts without titles, units, or axes create confusion → Ensure visuals are understandable without explanation 𝟏𝟎. 𝐍𝐨𝐭 𝐓𝐞𝐥𝐥𝐢𝐧𝐠 𝐚 𝐒𝐭𝐨𝐫𝐲 ↳ Data without narrative loses impact → Use insight-driven titles and annotations that answer "So what?" 𝐐𝐮𝐢𝐜𝐤 𝐜𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐡𝐚𝐫𝐢𝐧𝐠: → Right chart type → Honest scale → Accessible colors → Clear labels & context → One clear takeaway ⚡𝐏𝐫𝐨 𝐭𝐢𝐩: Show your visualization to someone unfamiliar with the data. If they need an explanation, simplify the chart. Which of these mistakes have you seen (or made)? ♻️Repost to help someone level up their data viz game Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

🍩 Practical Guide To Accessible Data Visualization. With useful pointers on how to design accessible charts and tables ↓ 🚫 Don’t rely on colors alone to communicate your data. ✅ Consider patterns or textures to distinguish bars and lines. ✅ For line charts, use different widths/dashes to set them apart. ✅ Place labels on lines, areas and pie charts directly. ✅ Make interactive visualization keyboard-accessible. 🚫 Don't rely on hover effects for explanations. ✅ Allow users to turn off animation and movements. ✅ Test in various screen sizes and zoom levels. ✅ Duplicate data from charts to the table format. ✅ Provide a text summary of the visualization. 🚫 Don’t mix red, green and brown together. 🚫 Don’t mix pink, turquoise and grey together. 🚫 Don’t mix purple and blue together. 🚫 Don’t use green and pink if you use red and blue. 🚫 Don’t mix green with orange, red or blue of the same lightness. ✅ Use any 2 colors as long as they vary by lightness. The safest bet is to never rely on colors alone to communicate data. Use labels, icons, shapes, rectangles, triangles, stars to indicate differences and show relationships. Be careful when combining hues and patterns: the pattern changes how bright or dark colors will be perceived. Use lightness to build gradients, not just hue. Make all interactive components accessible via keyboard. Add an option to explore data in a data table format. And always include people with accessibility needs not just in usability testing but in the design process. ✤ Useful resources Free Online Course On DataViz Accessibility (11 modules) https://lnkd.in/ejFYw5iA Intro To Accessible DataViz, by Sarah Fossheim https://lnkd.in/dEzvCsdP Data Viz Accessibility Resources, by Silvia Canelón, PhD Full list: https://lnkd.in/eM27dp7e Summary: https://lnkd.in/eGFKh4dk Colorblindness In DataViz, by Lisa Charlotte Muth https://lnkd.in/evn95YBp Accessibility-First Charts, by Kent Eisenhuth, Kai Salmon Chang https://lnkd.in/dnE2bfzZ Guidelines for DataViz Accessibility, by Øystein Moseng https://lnkd.in/epq5jwe6 Accessible Alternatives To Complex Charts, by Sheri Byrne-Haber (disabled) https://lnkd.in/eTJgvBWH Data Visualization Design Systems + Guidelines https://lnkd.in/dgADUDcz ✤ Tools For Accessible DataViz Highcharts: https://www.highcharts.com Datawrapper: https://www.datawrapper.de Viz-Palette: https://lnkd.in/e-JxgwHh Visa Charts: https://lnkd.in/e675Fsgr #ux #dataviz #accessibility

10 reasons why your dashboard lacks clarity. A - Don't put everything in one dashboard. => A dashboard made for everyone, is a dashboard used by no one. B - Help users see, not read. => "Good data visualization takes the burden of effort off the brain and puts it on the eyes." Stephen Few's C - Don’t use maps if they’re not relevant. => Even if your colleague worked so hard to get these ZIP codes. Ask yourself : Does the map add value to the business? D - Zoom in when necessary. => Sometimes (for specific reason) you'll need to truncate your axis. Because Usain Bolt has no intention of running the 100m in under 7 seconds. E - Declutter your charts. => It's a constant balance between space optimization and chart comprehension. F - Use double encoding on purpose. => Displaying the same KPI twice in the same chart may raise questions you don't want to hear during the kick-off meeting. Keep it clear. G - Rotate your charts to see full labels. => "My neck has been hurting lately, but I'm not sure why." H - Clean your pie chart. => Pie charts are hard enough to understand quickly, so let's not make them even trickier. I - Use aggregation to your advantage. => If your message is clear with 36 bars, why use 156? J - Use color to your advantage => The purpose of color is not to make your dashboard funky, but to attract the eye, to alert and to assist readability... Find this High Resolution visual + 50 other in the Dataviz Clarity Gallery here : https://lnkd.in/eThSWtWv #Businessintelligence #Datavisualization #DataAnalytics

Data analysts: if your visualizations look more complicated than your data, it's time for a reset! New data analysts often get caught up in creating fancy, overcomplicated visuals that can cloud the message. While fancy charts might look impressive, a straightforward bar chart often delivers insights much more clearly. Tips to Overcome Overcomplexity: 1. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗖𝗹𝗮𝗿𝗶𝘁𝘆: Ask yourself, “Does this chart help my stakeholders understand the data better?”     2. 𝗞𝗲𝗲𝗽 𝗜𝘁 𝗦𝗶𝗺𝗽𝗹𝗲: Bar or line charts will be the best fit for 80% of your use cases.     3. 𝗧𝗲𝘀𝘁 𝗬𝗼𝘂𝗿 𝗩𝗶𝘀𝘂𝗮𝗹𝘀: Get feedback. If the audience struggles to interpret your chart, it’s time to simplify it further. Your goal as an analyst is to present your results in an easily digestible form to your stakeholders so that they can make informed decisions. What’s your strategy for keeping your charts clear and impactful? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you've seen too many complex charts. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #datavisualization #simplicity #careergrowth

Most plots fail before they even leave the notebook. Too much clutter. Too many colors. Too little context. I have a stack of visualization books that teach theory, but none of them walk through the tools. In Effective Visualizations, I aim to fix that. I introduce the CLEAR framework—a simple checklist to rescue your charts from confusion and make them resonate: Color: Use color sparingly and intentionally. Highlight what matters. Avoid rainbow palettes that dilute your message. Limit plot type: Just because you can make a 3D exploding donut chart doesn’t mean you should. The simplest plot that answers your question is usually the best. Explain plot: Add clear labels, titles. Remove legends! If you need a decoder ring to read it, you’re not done. Audience: Know who you’re talking to. Executives care about different details than data scientists. Tailor your visuals accordingly. References: Show your sources. Data without provenance erodes trust. All done in the most popular language data folks use today, Python! When you build visuals with CLEAR in mind, your plots stop being decorations and start being arguments—concise, credible, and persuasive.

Bad data visualization is everywhere — here’s how to fix it. Understanding the essentials of effective data visualization is one thing, but witnessing poor data visualization in practice offers the real lessons. Take a look at this chart, “How Baby Boomers Describe Themselves,” which had some fundamental errors. The major problem? It disregards the rule of relativity. The design implies the data forms a complete whole, yet the percentages total 243%. This clearly indicates the wrong visual format was selected. If respondents could choose multiple answers, the data should be shown as a grouped bar chart rather than being forced into a single human figure. Additionally, contrast is mishandled: • Size contrast is deceptive – Larger sections don’t correlate with larger values. • Color contrast is excessive – Every section demands attention, causing nothing to stand out. • Shape contrast is absent – The chart depends solely on color to distinguish categories, reducing clarity. • Annotations cause confusion – Instead of providing clarity, extra design elements divert attention from the main insights. So, how to fix it? Opt for the correct visual structure, use proportional sizes, apply color contrast wisely, introduce meaningful shape variations, and ensure annotations are purposeful. Bad data visualization doesn’t just appear cluttered. It misleads. Correcting it involves directing the audience to the right insights without making it a struggle. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Good research deserves good poster design. Here’s how to structure every section of your academic research poster They’ll teach you how to collect data. But no one teaches you how to present it. Here’s what academic research poster should include ——————————————— 𝗧𝗜𝗧𝗟𝗘 𝗢𝗙 𝗬𝗢𝗨𝗥 𝗥𝗘𝗦𝗘𝗔𝗥𝗖𝗛 → 1–2 lines only. → Make it specific, bold, and readable from 3 feet away. → Add your name(s), affiliations, and contact info (email or QR code to full paper). ——————————————— 𝗜𝗡𝗧𝗥𝗢𝗗𝗨𝗖𝗧𝗜𝗢𝗡 → 2–3 sentences on why this study matters → Use bullet points for major facts (e.g. disease burden, knowledge gap) → Optional: add one icon or small visual (e.g. world map if global) ——————————————— 𝗢𝗕𝗝𝗘𝗖𝗧𝗜𝗩𝗘𝗦 → Numbered list of research questions or hypotheses → Keep them short, clear, and preferably bolded ——————————————— 𝗠𝗘𝗧𝗛𝗢𝗗𝗢𝗟𝗢𝗚𝗬 → Study design (e.g. RCT, cohort, case-control) → Setting (country, site, year) → Sample population (eligibility, key demographics) → Variables (exposures, outcomes, confounders) → Data sources/tools (e.g. surveys, registries, labs) → Analysis plan (stats methods, software used) → Optional: one flowchart or timeline visual ——————————————— 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 → Table: Key characteristics (age, sex, baseline traits) → Graph 1: Your main outcome → bar, line, or forest plot → Text Summary: 3–4 numbered findings with clear metrics (p-values, CIs, effect sizes) → Visuals: Maps for geographical data; survival curves if time-to-event is critical → Label everything: axes, legends, and font readable from 3 feet away ——————————————— 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 → 2–3 bullet points interpreting the results → 1 bullet: main limitation → 1 bullet: key implication or recommendation ——————————————— 𝗖𝗢𝗡𝗖𝗟𝗨𝗦𝗜𝗢𝗡 → One sentence only → No new data; just your biggest takeaway or impact summary ——————————————— 𝗥𝗘𝗙𝗘𝗥𝗘𝗡𝗖𝗘𝗦 & 𝗔𝗖𝗞𝗡𝗢𝗪𝗟𝗘𝗗𝗚𝗠𝗘𝗡𝗧𝗦 → 2–3 most relevant citations → Funding sources and disclosure (if required) → Keep font tiny but readable up close ——————————————— If they have to squint, it’s not a poster; it’s a paragraph. Design it for clarity, not complexity. ♻️ Repost this to help a student, colleague, or conference team build better science communication. #AcademicPoster #ResearchDesign

Most charts get ignored. Great ones get remembered. If your data doesn’t spark clarity, it won’t drive action. You don’t need louder visuals. You need smarter storytelling. Here are 7 shifts to help your charts inform, engage, and stick: 1️⃣ Focus on what matters ➟ Cut out clutter and extras. ➟ Use only what drives understanding. 2️⃣ Remove visual noise ➟ Ditch the 3D, shadows, and flashy backgrounds. ➟ Keep attention on the message. 3️⃣ Make complex info simple ➟ Use clear layouts. ➟ Break things down, step by step. 4️⃣ Use color with purpose ➟ Choose colors for contrast, not decoration. ➟ Be mindful of accessibility. 5️⃣ Lead with the point ➟ Use the Pyramid Principle. ➟ Start with the insight, support it underneath. 6️⃣ Annotate the story ➟ Add callouts or notes to guide attention. ➟ Connect the dots for the viewer. 7️⃣ Keep your style consistent ➟ Fonts, layout, and colors should flow. ➟ Design is clarity, not decoration. The takeaway: Every graph, chart, and slide is a chance to lead through insight. Use structure to show the story—and make it stick. What’s one data mistake you see all the time? Drop it below. Let’s help each other improve our slides. 📌 Save this before your next presentation 🔁 Share with your team to sharpen their storytelling 👤 Follow Jay Mount for high-trust tips on data, clarity, and communication that moves people.

5 simple steps to create better charts (that you can implement immediately) You’ve gathered your data, run your analyses, and created your charts. But… looking at your charts… you feel like they could be better. The data is there, the information is accurate, but the key message doesn’t “pop.” How can you make your message more clear and compelling? Let’s walk through an example: Below is a chart showing U.S. users’ daily engagement with top social media platforms in 2023. Follow along to see how we can make it more attention-grabbing. 1) Begin with your standard chart -- Start with grayscale. -- Avoid using color just for decoration - color should be added intentionally later to highlight key information. 2) Arrange the categories from highest to lowest value -- Improve visual flow by guiding your audience's eyes. -- Note: if the categories follow a natural sequence (e.g., age ranges or months of the year), maintain their original order. 3) Remove the axes and place value labels directly on the chart -- Improve readability and reduce visual clutter. -- Note: This works best if the exact values are important and the chart has a manageable number of categories. 4) Use color to emphasize the key category -- Apply color strategically to draw attention to the most important category (or categories). -- All other categories should remain gray. 5) Include a brief explanation highlighting the main insight -- Don’t make your audience guess what's most important. -- Ensure your message is clear by including a brief explanation of the main insight. Voilà! —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Data science or data analytics without storytelling is void. You can do all the SQL, all the Python, all the modeling — but if the final insight is not communicated in the right visual form, the value is lost. This cheat sheet is a perfect reminder that choosing the right chart is not decoration — it is part of analysis. It breaks the decision down by purpose of insight: 1) Composition Waterfall, Progress bar, Pie, Gauge — great when you want to show parts contributing to a whole or target progress. 2) Comparison Bar charts, Row charts, Line charts, Combo charts — useful when comparing categories or trends over time. 3) Distribution & Relationship Histogram and Scatter plot — when you want to show how values are spread or how two variables interact. 4) Stage Analysis Sankey and Funnel — ideal for visualizing drop-offs or flow across process stages. 5) Single Value KPIs Number & Trend cards — best for dashboards where one metric needs to stand out with context. The skill is not in plotting a chart — the skill is in selecting the correct one for the question being asked. Your analysis is only as powerful as the clarity of how you present it. cc Metabase #DataAnalytics #DataScience #DataVisualization #StorytellingWithData #BI #Metabase #DashboardDesign #DecisionMaking