Characteristics of my favorite statistics talks

I’ve been going to/giving statistics talks for a few years now. I think everyone in our field has an opinion on the best structure/content/delivery of a talk. I am one of those people that has a pretty specific idea of what makes an amazing talk. Here are a few of the things I think are key, I try to do them and have learned many of these things from other people who I’ve seen speak. I’d love to hear what other people think. 


  1. I don’t like outline slides. I think they take up space but don’t add to most talks. Instead I love it when talks start with a specific, concrete, unsolved problem. In my favorite talks, this problem is usually scientific/applied. Although I have also seen great theoretical talks where a person starts with a key and unsolved theoretical problem. 
  2. I like it when the statistical model is defined to solve the problem in the beginning, so it is easy to see the connection between the model and the purpose of the model. 
  3. I love it when talks end by showing how they solved the problem they described at the very beginning of the talk. 


  1. I like it when people assume I’m pretty ignorant about their problem (I usually am) and explain everything in very simple language. I think some people worry about their research looking too trivial. I have almost never come away from a talk thinking that, but I frequently leave talks confused because the background material wasn’t clear. 
  2. I like it when talks cover enough technical detail so I can follow the basic algorithm, but not so much that I get lost in notation. I also struggle when talks go off on tangents, describing too many subproblems, rather than focusing on the main problem in the talk and just mentioning subproblems succinctly. 
  3. I like it when proposed methods are compared to the obvious straw man and one legitimate competitor (if it exists) on a realistic simulation/data set where the answer is known. 
  4. I love it when people give talks on work that isn’t totally finished. This type of talk is scary for two reasons: (1) you can be scooped and (2) you might not have all the answers. But I find that unfinished work leads to way more discussion/ideas than a talk about work that has been published and is “complete”. 


  1. I like it when a talk runs short. I have never been disappointed when a talk ended 10-15 min early. On the other hand, when a talk is long, I almost always lose focus and don’t follow the last part. I’d love it if we moved to 30 minute seminars with more questions. 
  2. I like it when speakers have prepared their slides and they have a clear flow and don’t get bogged down in transitions. For this reason, I don’t mind it when people give the same talk a bunch of places. I usually find that the talk is very polished.

In the era of data what is a fact?

The Twitter universe is abuzz about this article in the New York Times. Arthur Brisbane, who responds to reader’s comments, asks 

I’m looking for reader input on whether and when New York Times news reporters should challenge “facts” that are asserted by newsmakers they write about.

He goes on to give a couple of examples of qualitative facts that reporters have used in stories without questioning the veracity of the claims. As many people pointed out in the comments, this is completely absurd. Of course reporters should check facts and report when the facts in their stories, or stated by candidates, are not correct. That is the purpose of news reporting. 

But I think the question is a little more subtle when it comes to quantitative facts and statistics. Depending on what subsets of data you look at, what summary statistics you pick, and the way you present information - you can say a lot of different things with the same data. As long as you report what you calculated, you are technically reporting a fact - but it may be deceptive. The classic example is calculating median vs. mean home prices. If Bill Gates is in your neighborhood, no matter what the other houses cost, the mean price is going to be pretty high! 

Two concrete things can be done to deal with the malleability of facts in the data age.

First, we need to require that our reporters, policy makers, politicians, and decision makers report the context of numbers they state. It is tempting to use statistics as blunt instruments, punctuating claims. Instead, we should demand that people using statistics to make a point embed them in the broader context. For example, in the case of housing prices, if a politician reports the mean home price in a neighborhood, they should be required to state that potential outliers may be driving that number up. How do we make this demand? By not believing any isolated statistics - statistics will only be believed when the source is quoted and the statistic is described.  

But this isn’t enough, since the context and statistics will be meaningless without raising overall statisteracy (statistical literacy, not to be confused with numeracy).  In the U.S. literacy campaigns have been promoted by library systems. Statisteracy is becoming just as critical; the same level of social pressure and assistance should be applied to individuals who don’t know basic statistics as those who don’t have basic reading skills. Statistical organizations, academic departments, and companies interested in analytics/data science/statistics all have a vested interest in raising the population statisteracy. Maybe a website dedicated to understanding the consequences of basic statistical concepts, rather than the concepts themselves?

And don’t forget to keep rating health news stories!