In health care, the objective of data analysis is to support decisions to improve the quality of patient care, contain costs, and increase efficiency in service delivery. When disseminating statistical results it is not sufficient to simply declare statistical significance (or insignificance). Results must be interpreted in the problem context. For example, a pilot study on the use of fall mats in a memory care facility shows that the number of falls with injury is reduced by an average of one per month with statistical significance. This reduction in fall injuries, while statistically significant, must be evaluated for practical significance by health care professionals. Is the benefit from the reduction in fall injuries cost justified? What are the costs to install, maintain, and clean floor mats? Are there other less expensive alternatives to reducing falls? What risks, such as tripping hazard, are associated with the use of fall mats?

You should think carefully about what your results mean in the problem setting and offer your recommendations to decision makers, within the limitations of your domain knowledge. Your recommendations should be guided by the data and analysis rather than your opinions. You should also offer suggested next actions. Offering solid analysis and impartial recommendations will make you a valuable contributor to the enterprise.

Statistical results, poorly communicated, will not have an impact on the problem or the intended audience. You should craft your communications to meet the needs of your audience in the time or space allowed. Do not try to force 10 minutes of information into a 5 minute presentation. In some cases you will need to prepare different communications for different audiences (e.g., general public, scientific or professional community), taking into consideration their familiarity with the problem domain, terminology, and statistical analysis. For example, in conveying statistical results to the general public it may be sufficient to state that a result is statistically significant, while communicating the same result to a scientific audience may benefit by reporting a p-value.

The following guidelines can assist you in preparing effective data analysis presentations:

Your information will be best received if it is focused, concise, relevant, and delivered with enthusiasm.

The twelve cases presented in this book illustrate many of the concepts covered in this introduction. With the exception of one (Visualizing Influenza Activity), the cases focus on six different problems with several cases forming a sequence for each problem. This illustrates a problem-solving approach starting with basic descriptive analysis and building to more complicated statistical inference and modeling. All but one data set were obtained from open sources.