List some of the statistics regarding new editors (daily volume, those that go on to make at least one edit, those that go on to make more than five edits)
4K new accounts per day
1K go on to make at least one edit
Percentage of warning templates delivered by semi-automated tools and bots ... fraction delivered by Huggle
Walk through our approach to the problem and what we wanted to test
State our research questions and hypothesis
Define the tests that we executed including a clear description of the control and test groups in each, along with the effect we hoped to measure
Demo what a user sees when being warned by Huggle - define warning levels
Describe how the three day measurement period following the template was chosen
How we bucketed new users
Describe the Methodology
how we filtered users
use of the revision table and the mediawiki API to track users contacted via huggle
Regression models used - emphasize that t-tests may have also been useful and appropriate for this problem
describe the variables in the model and the significance of the results
Results and Discussion
State the findings with respect to the research questions
Discuss the implications of this (friendlier templates, the types of editors to target, future experiments, it would be worth mentioning E3 and how we approach this work at WMF)
Actionability, what can have we done to act on these findings to improve new editor engagement on Wikipedia
