In my field, the descriptive part of the report is extremely important because it sets the context for the generalisability of the results. For example, a researcher wishes to identify the predictors of traumatic brain injury following motorcycle accidents in a sample from a hospital. Her dependent variable is binary and she had a series of independent variables. Multivariable logistic regression allowed her to produce the following findings:
- no helmet use adjusted OR = 4.5 (95% CI 3.6, 5.5) compared to helmet use.
- all other variables were not included in the final model.
To be clear, there were no issues with the modelling. We focus on the value that the descriptive statistics can add.
Without the descriptive statistics, a reader cannot put these findings in perspective. Why? Let me show you the descriptive statistics:
age, years, mean (SD) 54 (2)
males, freq (%) 490 (98)
blood alcohol level, %, mean (SD) 0.10 (0.01)
You can see from the above that her sample consisted of older, intoxicated males. With this information the reader is able say what, if any, these results can say about injuries in young males or injuries in non-intoxicated riders or in female riders.
Please don't ignore descriptive statistics.