How can you present unbiased primary research data to experts of varying levels?

Article 1: "Crafting Comprehensive Research Presentations for Diverse Audiences" Article 2: "Navigating Technical Jargon: Bridging the Gap in Research Communication" Article 3: "Ensuring Objectivity in Research Communication: A Guide for Researchers"

I don’t think there is a one size fits all presentation when it comes to data. Scientists will want to know more in depth as opposed to policy makers for example, would want to know the summary, percentages, numerical etc. Overall though simplifying might be beneficial for the scientists presenting that data and might open new avenues. I think it’s probably best to have data presentation in different levels in a single presentation. By gauging the response from the crowd/audience the presenter might want to elaborate further as necessary.

When presenting primary research data to experts of varying levels, it is important to provide high-level summaries and visualizations that clearly convey the key findings and their significance, while also making the underlying data and methodology transparent and accessible to those wanting deeper examination. Charts, graphs, and infographics can succinctly illustrate patterns and relationships for general audiences, with links to more technical documentation for specialists seeking granular details. This balanced approach allows both novice and advanced analysts to engage with the work in a way suited to their background, helping ensure the broadest and most accurate interpretation and application of the insights across disciplines.

Picture your data as a vibrant canvas, your audience as the captivated observers. Instead of offering a static tableau, plunge them into an interactive experience. Utilize dynamic visualizations, live data feeds, and interactive demonstrations to invite your audience to actively explore and engage with your findings. This fosters a sense of ownership and involvement, transforming data into a shared intellectual odyssey. For more information; I think these might help you with it: - Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic -The Visual Display of Quantitative Information by Edward Tufte

The level of background information will vary based on the audience. The main thing though is regardless of audience you must be ready to explain your parameters. To make policy you need to know the components, to learn you must know the parameters and restrictions/limitations and if you’re talking to experts they will ask the questions that frame the results regardless so have that information up front if you want it to be unbiased and effective.

The right tool for the job, yes, but also don’t overload your audience. Using too many visuals, or large blocks of text, detracts from the overall message, particularly if you’re presenting verbally as well.

I believe that data presentations, though rooted in facts, hold the power to spark curiosity, ignite debate, and galvanize action. To truly resonate with your audience, transcend the role of mere information disseminator. Embrace storytelling techniques, draw upon relatable analogies, and pose thought-provoking questions to weave your data into an engaging narrative that resonates with their emotions and cognitive processes.

Graphs, charts, and infographics are a great way to bring data to life. These visualizations contextualize abstract findings like ratios, frequencies, comparisons, etc.

One way to lose your audience is to present data that doesn’t make sense. If you have a bar chart where the percentages should add up to 100%, make sure x + y + z = 100. It’s a pretty common mistake that I’m guilty of committing a time or two. Watch out for your data analysis software rounding your numbers. And let’s face it, human error is real.

Another important element with checking data quality is to have at least one peer review your work. When you're working large datasets, the risk or error is higher. This is especially critical if you're planning to share out data publicly. Always have your work peer-reviewed, even if you've been doing the work for a long time.

Everyone makes a single typing error or puts a datum point in the wrong place. Build in quality control measures as part of the planning and use them before analysis. Work edits from an edit sheet of your data and have a separate master sheet for the moment you’re experimenting and above all ensure that you don’t taint your data through rounding or “close enough”

Three things I've found helpful: 1. Script your intro. Establish the narrative for why people are there, give the audience a sense of direction, tell them what's in it for them. 2. Write the bumper sticker/headline for each slide. At a minimum, paraphrase the slides main point in your own words. This is your headline. 3. Map the page: I use visual cues, numbers or letters on my rehearsal presentation to train myself on how I want to navigate each slide with a client. This is particularly helpful for dense eye charts. And always orient the audience towards the visual (e.g. position of elements, chart labels and legends, rows and columns, etc.).

I always keep in mind the key points below, hope it'll help you too: Polish your presentation to perfection, where words and emotions dance and engagement takes flight. The rehearsal room is your domain, where ideas find their voice and eloquence takes root. Plan meticulously, rehearse passionately, and adapt seamlessly. Orchestrate your delivery, captivate your audience, and adapt to their perspectives. Welcome feedback, embrace diversity, and practice with fervor. Let practice be your chisel, shaping your presentation into a masterpiece.

One thing I have found helpful in a team setting is to mock the presentation. This act of presenting to one's team (who are to ensure they come up with important questions and critiques) helps you figure out which bits of the presentation are unclear and could do with some clarity. It also helps you master the delivery of the presentation, especially if it can be rehearsed more than once. A pro tip is to task each team member with asking a set number of questions (two will do fine) whenever the mock presentation happens.

It is important to consider if the data being presented tells the story. Not accurately, not fine tuned, but the story as it was found. While this may not be groundbreaking, it is a step in decolonization. Particularly when researchers are working with qualitative data.