The most important guiding principle is to tailor the information you deliver to the objective of the communication. For a targeted question aimed at getting clarification about the coding of a variable, the recipient of your communication does not need to know the overall objective of your analysis, what you have done up to this point, or see any figures or tables. A specific, pointed question along the lines of “I’m analyzing the crime dataset that you sent me last week and am looking at the variable”education" and see that it is coded 0, 1, and 2, but I don’t see any labels for those codes. Do you know what these codes for the “education” variable stand for?"
For the second type of communication, in which you are seeking feedback because of a puzzling or unexpected issue with your analysis, more background information will be needed, but complete background information for the overall project may not be. To illustrate this concept, let’s assume that you have been examining the relationship between height and lung function and you construct a scatterplot, which suggests that the relationship is non-linear as there appears to be curvature to the relationship. Although you have some ideas about approaches for handling non-linear relationships, you appropriately seek input from others. After giving some thought to your objectives for the communication, you settle on two primary objectives: (1) To understand if there is a best approach for handling the non-linearity of the relationship, and if so, how to determine which approach is best, and (2) To understand more about the non-linear relationship you observe, including whether this is expected and/or known and whether the non-linearity is important to capture in your analyses.
To achieve your objectives, you will need to provide your audience with some context and background, but providing a comprehensive background for the data analysis project and review of all of the steps you’ve taken so far is unnecessary and likely to absorb time and effort that would be better devoted to your specific objectives. In this example, appropriate context and background might include the following: (1) the overall objective of the data analysis, (2) how height and lung function fit into the overall objective of the data analysis, for example, height may be a potential confounder, or the major predictor of interest, and (3) what you have done so far with respect to height and lung function and what you’ve learned. This final step should include some visual display of data, such as the aforementioned scatterplot. The final content of your presentation, then, would include a statement of the objectives for the discussion, a brief overview of the data analysis project, how the specific issue you are facing fits into the overall data analysis project, and then finally, pertinent findings from your analysis related to height and lung function.
If you were developing a slide presentation, fewer slides should be devoted to the background and context than the presentation of the data analysis findings for height and lung function. One slide should be sufficient for the data analysis overview, and 1-2 slides should be sufficient for explaining the context of the height-lung function issue within the larger data analysis project. The meat of the presentation shouldn’t require more than 5-8 slides, so that the total presentation time should be no more than 10-15 minutes. Although slides are certainly not necessary, a visual tool for presenting this information is very helpful and should not imply that the presentation should be “formal.” Instead, the idea is to provide the group sufficient information to generate discussion that is focused on your objectives, which is best achieved by an informal presentation.
These same principles apply to the third type of communication, except that you may not have focused objectives and instead you may be seeking general feedback on your data analysis project from your audience. If this is the case, this more general objective should be stated and the remainder of the content should include a statement of the question you are seeking to answer with the analysis, the objective(s) of the data analysis, a summary of the characteristics of the data set (source of the data, number of observations, etc.), a summary of your exploratory analyses, a summary of your model building, your interpretation of your results, and conclusions. By providing key points from your entire data analysis, your audience will be able to provide feedback about the overall project as well as each of the steps of data analysis. A well-planned discussion yields helpful, thoughtful feedback and should be considered a success if you are left armed with additional refinements to make to your data analysis and thoughtful perspective about what should be included in the more formal presentation of your final results to an external audience.