# Econstudentlog

It’s been a long time since I had one of these. Questions? Comments? Random observations?

I hate posting posts devoid of content, so here’s some random stuff:

i.

If you think the stuff above is all fun and games I should note that the topic of chiralty, which is one of the things talked about in the lecture above, was actually covered in some detail in Gale’s book, which hardly is a book which spends a great deal of time talking about esoteric mathematical concepts. On a related note, the main reason why I have not blogged that book is incidentally that I lost all notes and highlights I’d made in the first 200 pages of the book when my computer broke down, and I just can’t face reading that book again simply in order to blog it. It’s a good book, with interesting stuff, and I may decide to blog it later, but I don’t feel like doing it at the moment; without highlights and notes it’s a real pain to blog a book, and right now it’s just not worth it to reread the book. Rereading books can be fun – I’ve incidentally been rereading Darwin lately and I may decide to blog this book soon; I imagine I might also choose to reread some of Asimov’s books before long – but it’s not much fun if you’re finding yourself having to do it simply because the computer deleted your work.

Here’s the abstract:

“Statistical power analysis provides the conventional approach to assess error rates when designing a research study. However, power analysis is flawed in that a narrow emphasis on statistical significance is placed as the primary focus of study design. In noisy, small-sample settings, statistically significant results can often be misleading. To help researchers address this problem in the context of their own studies, we recommend design calculations in which (a) the probability of an estimate being in the wrong direction (Type S [sign] error) and (b) the factor by which the magnitude of an effect might be overestimated (Type M [magnitude] error or exaggeration ratio) are estimated. We illustrate with examples from recent published research and discuss the largest challenge in a design calculation: coming up with reasonable estimates of plausible effect sizes based on external information.”

If a study has low power, you can get into a lot of trouble. Some problems are well known, others probably aren’t. A bit more from the paper:

“design calculations can reveal three problems:
1. Most obvious, a study with low power is unlikely to “succeed” in the sense of yielding a statistically significant result.
2. It is quite possible for a result to be significant at the 5% level — with a 95% confidence interval that entirely excludes zero — and for there to be a high chance, sometimes 40% or more, that this interval is on the wrong side of zero. Even sophisticated users of statistics can be unaware of this point — that the probability of a Type S error is not the same as the p value or significance level.[3]
3. Using statistical significance as a screener can lead researchers to drastically overestimate the magnitude of an effect (Button et al., 2013).

Design analysis can provide a clue about the importance of these problems in any particular case.”

“Statistics textbooks commonly give the advice that statistical significance is not the same as practical significance, often with examples in which an effect is clearly demonstrated but is very small […]. In many studies in psychology and medicine, however, the problem is the opposite: an estimate that is statistically significant but with such a large uncertainty that it provides essentially no information about the phenomenon of interest. […] There is a range of evidence to demonstrate that it remains the case that too many small studies are done and preferentially published when “significant.” We suggest that one reason for the continuing lack of real movement on this problem is the historic focus on power as a lever for ensuring statistical significance, with inadequate attention being paid to the difficulties of interpreting statistical significance in underpowered studies. Because insufficient attention has been paid to these issues, we believe that too many small studies are done and preferentially published when “significant.” There is a common misconception that if you happen to obtain statistical significance with low power, then you have achieved a particularly impressive feat, obtaining scientific success under difficult conditions.
However, that is incorrect if the goal is scientific understanding rather than (say) publication in a top journal. In fact, statistically significant results in a noisy setting are highly likely to be in the wrong direction and invariably overestimate the absolute values of any actual effect sizes, often by a substantial factor.”

iii. I’m sure most people who might be interested in following the match are already well aware that Anand and Carlsen are currently competing for the world chess championship, and I’m not going to talk about that match here. However I do want to mention to people interested in improving their chess that I recently came across this site, and that I quite like it. It only deals with endgames, but endgames are really important. If you don’t know much about endgames you may find the videos available here, here and here to be helpful.

iv. A link: Crosss Validated: “Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.”

A friend recently told me about this resource. I knew about the existence of StackExchange, but I haven’t really spent much time there. These days I mostly stick to books and a few sites I already know about; I rarely look for new interesting stuff online. This also means you should not automatically assume I surely already know about X when you’re considering whether to tell me about X in an Open Thread.

November 18, 2014 - Posted by | Chess, Lectures, Mathematics, Open Thread, Papers, Statistics