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Read each of these readings and respond to the questions for each reading.
Wood, 2013
The blog post here (http://ecodevoevo.blogspot.com/2013/05/lets-abandon-significance-tests.html) was written by James Wood, a prominent bioanthropologist and demographer at Penn State, whose prolific research career has broadly covered the biological dimensions of human fertility and mortality in cross-cultural perspective. Because this is a blog post rather than a peer-reviewed publication, Wood is very passionate, sometimes hyperbolic, and occasionally sarcastic in his discussion of NHST. What is so interesting about this post is Wood’s comments on his own evolving relationship with frequentist statistics (the statistical framework out of which NHST grew) near the end of the post. Read this blog post, and answer the following questions:
How is Wood’s perspective on frequentism changing? In other words, in what alternative direction is he drawn, and what role does his skepticism about NHST play in this change?
While we have focused primarily on the level of significance α in class, Wood also discusses another value, b. What is this value, and how might we better perform significance tests when we include this value as a part of our inference along with α?
Trafimow and Marks, 2015
In 2015, one journal, Basic and Applied Social Psychology, banned the publication of papers using NHST. This decision received a lot of attention among the wider scientific community. The journal’s announcement of and reasoning behind this policy are presented by an editorial by Trafimow and Marks. Read their paper here
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and answer the following questions:
What is the main problem with NHST(P), according to these authors?
What other major kind of statistical inference did the journal ban, and why?
What alternative statistical approaches to research do the authors suggest, and why do they believe that these alternative approaches are better than the banned approaches?
Are you convinced that these alternative approaches will work better than the banned approaches? Why or why not?
Wasserstein and Lazar, 2016
The ongoing crisis of confidence in NHST came to a head in 2016, leading to the publication of a statement about this framework by the American Statistical Association (ASA) in their journal The American Statistician. The statement was prepared by a large group of statisticians from many different schools of statistical thought, not all of whom use significance levels in their own work or teach it to their students. This statement was intended to serve as an authoritative review of what NHST is, what it can do for scientific research, and what its limitations are. This statement is more nuanced than the other two readings above, and it was prepared by statisticians to provide guidance for researchers to avoid the misuse of NHST, without entirely abandoning the framework. The first two pages in the PDF copy of this statement review the history of this crisis, while the remaining two pages provide qualified commentary on significance, p-values, NHST, and other related matters. Read this paper here
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, and answer the following questions:
Rather than recommending the abandonment of NHST and its ingredients (p-values, levels of significance, etc.), what does this statement recommend that researchers do?
All three of the readings discussed here mention a topic called “effect size,” and they contrast statistical significance with “scientific, human, or economic significance.” (Also review your textbook, page 199, on “practical versus statistical significance.”) Paradoxically, while effect size seems important to the authors of all of these readings, they don’t provide any really great definition of this concept. (In fact, Trafimow and Marks are wrong to discuss this concept under the heading of descriptive statistics.) The ASA’s statement comes closest to defining this concept (page 132). To the best of your ability based on these three readings, what do you think the meaning of “effect size” is?
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