What is the point of reporting descriptive statistics? I have just carried out an analysis of my data using logistic regression however I am also required to have a descriptive Statistics part in my report. I honestly don't see the point in this and I was hoping that someone might be able to explain why it is necessary.
For example if I plot a histogram of one of my independent continuous variables and it shows normality or it shows skewness how will this add any value to the report?
My data consists of a dependent variable true or false of getting a job and the independent variable is grades in mid-term, grades in final exams, and male or female. 
 A: 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.
A: Another thing is to show how well behaved your variables are. If, for example, one of your variables is the salary, and you have interviewed exactly one billionaire, when you input his salary into the logistic regression is going to dominate over everything else, so you will likely learn to ignore the salary, regardless of how much actual information it may hold.
Some methods are more sensitive than others to skewness and extreme values, and logistic regression is rather on the sensitive side. Of course, the final proof is in the pudding, and you can compare the results obtained with the raw data, or with each feature transformed towards normality.
A: The point of providing descriptive statistics is to characterise your sample so that people in other centres or countries can assess whether your results generalise to their situation. So in your case tabulating the sex, grades and so on would be a beneficial addition to the logistic regression. It is not to enable people to check your assumptions although they may try to do that too.
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Edit to give links to some guidelines used in health
In the field with which I am familiar, health, there are specific guidelines for reporting. These have been collected together in the EQUATOR network which should be consulted for up to date details.
As an example we may take clinical trials where the relevant guideline is CONSORT. In the document outlining the guideline available here and elsewhere we read in Table 1 recommendation 15 "A table showing baseline demographic and clinical characteristics for each group".
There are similar recommendations for other study types.
A: A descriptive part helps to understand the reader your dataset. In applied econ it is usually highly recommended as it may show the first potential flaws in your analysis.
You may use data from different sources to blow up your descriptives.
1 table should be enough. The one you attached is not very intuitive.
