UV4Plants Bulletin 2016:1
The previous issue is also available as a PDF file
Seeing in UV
[twenty20 img1=”878″ img2=”877″ offset=”0.25″] Move the slider to compare the images. Photographs by Titta Kotilainen (c) 2016, all rights reserved.
Follow-up to the statement on p-values by ASA
The American Statistical Association Says [Mostly] No to p-values Norman Matloff has published a new post after receiving criticism and comments about stating “The ASA says No to p-values” in his post I wrote about some days ago. He defends his interpretation in this new post. However, I think, the interpretation of the statement in a context different from the “Big data” field to which he is used to does not need to always be “Says No to p-values” but instead in many cases could be “Use p-values to assess the strength of the evidence and nothing else”. However, “tests” with binary outcomes on probabilities that are essentially continuous, will always be based on an arbitrary threshold and discard a great deal of information. Consequently to me using as suggested by Norman Matloff “assess” in place of “test” makes a lot of sense. The new post is at https://matloff.wordpress.com/2016/03/09/further-comments-on-the-asa-manifesto/ “Itʹs Not The P-valuesʹ Fault” Joint post by Yoav Benjamini and Tal Galili. The post highlights points raised by Yoav in his official response to the ASA statement… Source: It’s Not The P-values’ Fault – Reflections On The Recent ASA Statement (+relevant R Resources) | R-statistics Blog P-values: the Continuing Saga I highly recommend the blog post by Yoav Benjamini and…
Grants available from ESP
I have received this message from ESP through Francesco Ghetti. I have been in touch with Francesco and our conference is eligible for this support. This does not mean any assurance of funding but it looks like a good opportunity for those in need of funding to send an application.
The American Statistical Association Says No to p-values
This post is copied from my Blog for Students and is to some extent off-topic but relevant to anybody doing research. Norman Matloff (2016) writes in his post: Sadly, the concept of p-values and significance testing forms the very core of statistics. A number of us have been pointing out for decades that p-values are at best underinformative and often misleading… Source: After 150 Years, the ASA Says No to p-values | Mad (Data) Scientist Yesterday, the statement by the American Statistics Association was published on-line in the journal “The American Statistician”. Many statisticians have been aware of the problems of significance tests for a long time, but general practice, teaching and journal instructions and editors’ requirements had not changed. Let’s hope the statement will start real changes in everyday practice. John W. Tukey (1991) has earlier written quite boldly about the problem: Statisticians classically asked the wrong question—and were willing to answer with a lie, one that was often a downright lie. They asked “Are the effects of A and B different?” and they were willing to answer “no.” All we know about the world teaches us that the effects of A and B are always different—in some decimal place—for any A and B. Thus…