Here's how you can convey the meaning and relevance of statistical findings as a statistician.

Provide background information and context to help your audience understand the significance of the statistical findings within the broader context of the research or problem area. Avoid jargon and technical terms, and explain statistical concepts in simple, understandable language. Use analogies or real-world examples to illustrate complex ideas. Encourage active participation and engagement by asking questions, soliciting feedback, and fostering discussion. Tailor your communication style to the needs and preferences of your audience to ensure that they remain engaged and interested.

As a statistician, it's vital to convey statistical findings meaningfully. Simplify complex concepts, provide context, and emphasize relevance to the audience. Use clear visuals and storytelling to make findings memorable and actionable. Foster discussions to deepen understanding and encourage collaboration.

Based on my experience, it is possible to tailor your presentation style and language to meet the audience's comprehension. Provide explanations that are straightforward and simple for beginners, while providing more technical details for those with more advanced knowledge. In order to explain statistical concepts, it is important to use everyday language and focus on real-world examples that are relatable and easy to grasp

All good communication takes into account the fact that humans understand and remember stories better than a collection of facts. This is why even in a results section of a paper, you should always be using the data to help weave the story of what your findings mean, including what you expected to find and whether you found support for the hypotheses. A big part of what I teach grad students is that the statistics aren't even central stage when writing your results section; they are merely the supporting characters that allow your findings (the core of the story) to have substance.

Data exists in the context of the domain that you are working in. It is not enough to report the statistical likelihood for why your hypothesis is correct. It is not enough to discuss the test statistics, the confidence interval, etc. You must be able to communicate why these things are important to your audience. Which one of these sounds better? "We can see X has this much of an impact on Y. Here are the relevant statistics in my report." "We can see from the data that changing the color of this button has a significant impact on whether customers clicked this button. This is likely to increase engagement on this new webpage and, therefore, the number of sales for this product in the upcoming quarter."

I have a slightly different take on the matter, even though I completely support the need of better interpretation of complex models and statistical findings. But at the same point, whomever the numbers are being prepared and projected for (maybe a people manager) must possess the basic skill of statistical understanding and the culture of nurturing statistical fundamentals across levels especially, in an analytics/data science domain must be a mandate. People in the top of the chain should not only preach it but practice too, its truly sad when I come across such people calling event rate as precision.

As a statistician, effectively communicating the meaning and relevance of statistical findings is paramount. It involves translating complex data into actionable insights that resonate with diverse audiences. Utilizing clear visuals, storytelling techniques, and real-world examples can enhance comprehension and engagement. Additionally, fostering open dialogue and addressing uncertainties or limitations in the findings promotes transparency and builds trust. In my humble opinion, mastering these skills empowers statisticians to drive informed decision-making and maximize the impact of their analyses.