What form of reviewer/author guidelines do we want? And how prescriptive? [was Re: Articles and Other Useful Statistical Resources]

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Matthew Kay

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May 29, 2016, 7:55:17 PM5/29/16
to Pierre Dragicevic, David Lovis-McMahon, Transparent Statistics in HCI
(Pierre, thanks for elevating this conversation to the list. I've renamed the subject to better reflect its contents) 

I think that Paul Resnick raised some good questions in comments on the reviewer guidelines that are worth a more substantive conversation. Part of that conversation is about how strong or strict our guidance should be. Playing devil's advocate, there may be a danger in making a document that appears to accept any statistical practice. I do not think that's what we are trying to do (and I do not think it's what we have now), but on the continuum from "love and peace, everyone do what you want" and "do X and only X", we need to articulate where we want to be and make that consitent. 

Maybe we just need to do a better job of stating that the high-level bit of the document is not "do what you want". Or maybe there are more substantial changes needed. Even if preventing misguided rejections (Pierre's point #2) and rewarding good practices (#3) are short-term goals, I think there may be something productive to be done towards improving the rigor of reviews (#1), and maybe there are some stronger recommendations we can make. 

Brainstorming: do we want a consistent mechanism to explicitly label some practices as "bad", "acceptable", "best"? Possibly crazy idea: one could imagine guidelines that are designed explicitly to deal with inertia, patterned after deprecation in APIs: something like, "Practice X is acceptable in version 1.1 of this document (up to CHI 2019), but will become obsolete in version 2.0 of this document (CHI 2020). As of that time, consider Practice Y or Z". This gives people time to adjust, and an explicit mechanism for introducing changes to practice. This could be a hairy process, and we would have to be very careful about finding consensus on what practices are made obsolete, but I think there is some potential value to it.

There is a related question of audience and use case. These dictate somewhat what the form of the recommendations document should be.

Here are some possible use cases:

#1. A document that a reviewer consults (possibly in the manner of an FAQ) to get a sense of how to evaluate a particular aspect of a study. E.g., perhaps I am reviewing a paper and I have some questions about the removal of outliers in a study. I could consult the guidelines document and read that section. This might allay concerns I had about the paper (if the authors have given the necessary detail asked for in the reviewer guidelines), or it might prompt me for questions to ask in my review. If this is a use case we want to support, the document will need a clear table of contents as a "way in" and perhaps a section on "using this document".

#2. A document that a reviewer reads top-to-bottom before doing reviews. I doubt very much we will ever achieve a high number of people reading the document all the way through, just as I doubt the existing CHI reviewer guidelines are fully read by a majority of reviewers. A shorter document, or a pithy intro and general set of principles might be the most we can hope a larger number of people will read in detail.

#3. A document used by authors to a) help them properly present their results and b) defend their statistical practices when writing their papers. (a) would suggest there is value in including examples within the document (which I think would be nice---especially for us to suggest some graphical alternatives to impenetrable tables of values). The form of the document for this use case might be similar to that for #1, in that an author might be more likely to index into the document like a FAQ in order to gain suggestions for presenting their work and even citations for defending it.

#4. A document that authors read in order to guide them in how to conduct their analyses. It is debatable whether this is beyond the scope of this document. At the very least, this document can act as a place to collect references to other resources that may help authors here (such as that suggested by David). Already we have a number of citations in the reviewer guideline document. If an author reads the guidelines in a particular section that they want to learn more about, that section should point them at the necessary material to do so.

The current document is perhaps heavily skewed towards use case #1. Though, it probably needs better organization to make it really useful there. I think that supporting the other use cases is possible to varying degrees, as described above. 

Other thoughts? What have I missed? 

---Matt


On Sun, May 29, 2016 at 1:47 AM, Pierre Dragicevic <pierre....@gmail.com> wrote:
Thanks David, this seems to be an excellent resource to cite in the guidelines.

I suppose it could be useful at some point to discuss what these guidelines should be trying to achieve. For now I see three possible goals.

1. Moving towards reviews that are more rigorous and less forgiving of errors in statistical analyses/interpretations. This would be desirable but as we all keep pointing out, we're lacking statistical expertise in the reviewing pool. So I don't think we should be  too obsessed with this goal. Educational material and resources that discuss common statistical errors are plentiful and we should encourage reviewers to read them, but I don't think the CHI guidelines necessarily need to repeat these.

2. Moving towards reviews that recognize and reward good practices that are not widely recognized as such at CHI. Things like clarity and completeness, nuanced conclusions, shared material, etc. are all easy to assess even by non-expert reviewers, and if reviewers are properly educated on the importance of these, this could contribute to improving the quality and transparency of reports overall and reduce practices like p-hacking.

3. Moving towards reviews that do not reject statistical reports for the wrong reasons. I'm not sure why this one is so much overlooked. For a non-expert and/or hurried reviewer, it is tempting to use simple heuristics to assess the validity of a statistical report (e.g., does it report ANOVAs / p-values? Are the results significant? Is sample size more than X?) rather than looking at the subtleties of the analysis or at the big picture. As long as reviewers will believe in such heuristics, other recommendations will have little influence.

My hope is that we can encourage reviewers to replace their old, misguided heuristics (3) with other, better heuristics (2) for evaluating studies. Covering (3) is difficult and we may not all agree, but it seems fairly easy to list the different ways we address concrete statistical problems, and state that they're all valid. Such a statement may seem vague as recommendation to authors, but as a recommendation to reviewers it is quite specific, because it implies that using method X rather Y shouldn't be a reason for rejection.

Pierre


On Sun, May 29, 2016 at 7:56 AM, David Lovis-McMahon <dlo...@gmail.com> wrote:
I believe developing statistical guidelines for the HCI community can be furthered by providing a resource for both authors and reviewers. To that end, I thought it might be good to get that ball rolling with a fresh epidemiology methods paper by Sander Greenland and colleagues (see attached) Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2015). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 1-14.

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ROBERTSON Judy

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May 30, 2016, 3:18:32 AM5/30/16
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Hey

 

 

I like Matt’s idea of “Practice X is acceptable in version 1.1 of this document (up to CHI 2019), but will become obsolete in version 2.0 of this document (CHI 2020). As of that time, consider Practice Y or Z". An advantage of it is that people won’t want to seem out of date, whereas they might argue indignantly that their practice wasn’t “bad”.

Ideas #1 and #3 will be very useful. Idea #2 might be useful for editors or meta-reviewers. We can’t expect all reviewers to read a long document but we can expect editors to be on top of their game. Do HCI journals use specialist statistical reviewers?

 

 

Judy

Sean Munson

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May 30, 2016, 2:49:59 PM5/30/16
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Of the suggested uses, I would prioritize a guide written for the authors (#3). I’d rather encourage and promote good practices, and tools that make them possible, over setting a higher bar in the review process without helping the community meet it. I’m still in favor of a section written for the reviewers’ perspective (#1) as well — but it would be best, perhaps, to start with what we think is reasonably well supported for authors in the CHI community.  

On obsoleting practices, I’d suggest a softer and approach. In a document that only has an effect through its ability to persuade, language that motivates authors to move to better practices and that encourages reviewers to ask why a better practice was or was not used seems more appropriate than language that is more absolutest or that implies this document has greater clout than it does. 

That would also allow for more discussion of the state of “bad” practices and the barriers to moving away from them. Is a bad practice still widely used because the tools to do better are not yet widely available? Not yet usable by anyone other than the people who created them? Have there been recent developments that make something formerly good-but-tedious more accessible? 

sean

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Matthew Kay

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May 30, 2016, 5:29:55 PM5/30/16
to Sean Munson, Pierre Dragicevic, ROBERTSON Judy, Transparent Statistics in HCI, David Lovis-McMahon
Of the suggested uses, I would prioritize a guide written for the authors (#3). I’d rather encourage and promote good practices, and tools that make them possible, over setting a higher bar in the review process without helping the community meet it. I’m still in favor of a section written for the reviewers’ perspective (#1) as well — but it would be best, perhaps, to start with what we think is reasonably well supported for authors in the CHI community.  

I think that the point about setting a high bar in reviews without the support to meet it is a good one.

Perhaps this is one way to look at things:

- The main goal (drawing from Pierre) in creating a reviewer guide might be to pre-empt reviewers from rejecting better statistical practice just because it is unfamiliar to them. So we could create a reviewer guide focused just on describing some variety of good, possibly unfamiliar, approaches. Or even just a list of "bad reasons to reject a paper". This can be cited by authors in papers and rebuttals.

- The goals of creating an author guide might be to (a) expose authors to better practices, (b) suggest more effective and transparent ways of communicating results, and (c) point them at tools and literature for putting those approaches into practice. Thus it might include examples of good practice, possibly with pointers to papers (e.g., examples of turning tables into graphs) and/or code to generate the graphs in question.

I think that scoping may be an issue with an author guide that will need to be resolved up front (else it will balloon into a book on statistical methods, which is too big for what we want). One scope is to go back to a focus on clarity and transparency: what methods are more transparent, and what ways of communicating results are more transparent? This begins to scope such a guide. It could also give a way to index: So you want to communicate the results of an ANOVA? We suggest asking yourself: do you really want an ANOVA, or are your research questions are actually about regression coefficients or pairwise estimates? If so, consider a regression and a coefplot and/or plots of pairwise comparisons instead of an ANOVA and an ANOVA table. Here's an example of how!

Perhaps this is a way to get the softer approach Sean is suggesting?

Finally, in creating these guides we should also consider what we don't want to be, and what we could do better. I Googled for "APA statistics reporting" and got this document: http://evc-cit.info/psych018/Reporting_Statistics.pdf. It has endlessly persnickety recommendations about just how to format the results of statistical tests. Meanwhile, they recommend impenetrable prose and tables as ways of presenting results. They formalize the minutiae of reporting but have lost the big picture of communicating results clearly.

---Matt




David Lovis-McMahon

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May 31, 2016, 2:19:22 PM5/31/16
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I want to amplify the idea of an author’s guide and suggest that a reviewer’s guide is just as if not more important.

 

In my experience as the methods expert reviewing articles across disciplines (psychology, law, and anthropology), it is important to remember the reviewer’s role as a gatekeeper. Research in procedural justice and legitimacy suggest that people will oppose having blind reviewers using what is perceived to be arbitrarily decided rules. Anecdotally, I think that part of what has slowed the advance of methods in psychology has been the sense among substantive researchers that methodologists have recently been playing a game of “Aha! Gotcha!” This fosters a combativeness that invites further division along methodological grounds and stymies productive discussion.

 

To prevent that perception, I think guidelines about the review process are just as, if not more, important than guidelines for the authors. In the same way as having transparency in the legal system, having clear reviewer guidelines promotes trust, reduce anxiety on the part of authors, and reduces the potential for perceived unfairness and bias on the part of the reviewer.

 

In balancing those factors, I’ve had a lot of success with the following guide that was born out of my Experimental and Quasi-Experimental Design seminar and my time in law school. (I should note that I have yet to do a review in HCI so I’m not certain how well what I’ve done in the past maps onto the current review process in HCI).

 

My methods seminar was focused on the Cambelian framework (William R.. Shadish, Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Wadsworth Cengage learning). So every review I’ve written has incorporated four sections: Statistical Conclusion Validity, Internal Validity, Construct Validity, and External Validity. Basically, are the stats done correctly, was the study design compromised by confounding variables, did the study actually manipulate and measure the theoretical construct claimed by the researcher, and does the study design support generalizing the reported effect to other groups of participants, times, or situations. Each form of validity is predicated on the prior form being true. That is, Internal Validity cannot be guaranteed if the Statistical Conclusion Validity is suspect. However, as the gatekeeper it is important to assess all four kinds of validity under the assumption that the prior form is true.

 

In my approach, the goal of the methods reviewer is to establish whether the statistical and design evidence offered in the article is valid. Moreover, the decision to accept or reject on methods grounds applies the rule that the identified error must undermine or change the author’s conclusion. That is, as a gatekeeper it is not sufficient to point out a statistical or methodological error. It is the gatekeeper’s job to establish how that the error undermines the author’s position. By placing the burden on the reviewer to establish the harm to the author’s conclusions, the rule acts in a permissive manner. That is, if I didn’t like the use of Bayesian estimation, I’d have to establish how its use in a particular analysis undermines the author’s conclusions.

 

I believe this is also why it is helpful to have a repository of methods papers that operate at both a general level like the one I posted before as well as those that focus on a specific method like selecting priors for Bayesian estimation. This way errors uncovered by reviews can become part of the common body of methodological knowledge and hopefully prevent them from occurring in the future.


-David

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Mike Glueck

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May 31, 2016, 3:12:07 PM5/31/16
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I agree there is value in both guidelines to support authors, as well as reviewers.

I believe these guides should fundamentally seek to promote an awareness of best practices and hopefully foster a sensitivity to why certain aspects of statistical reporting are important.

I think the shorter these documents are, the more likely it will be that they are used.  It may be worthwhile to curate a more in-depth reference document for those who are interested, but I think a lot can be accomplished by a concise, example-driven primer.

A caveat, of course, in writing something too prescriptive, is that we may not achieve the goal of actually educating (which I think is an implicit goal).  If we only write a recipe for authors and reviewers to follow, they may not gain a deeper understanding of why they are doing it.

Personally, I am really compelled by example-based approaches.  I think it helps put abstract concepts into context which makes the principles easier to apply.  A document of general guidelines places the onus of applying the principles on the readers, who may or may not be equipped to do so effectively.  Perhaps we could curate examples of both preferred and less-preferred reporting styles that have been published in our field.  To Sean's point, speaking in terms of preferred/less-preferred may help soften these qualifications.  Some older work may very well still be good and relevant, but the way we would approach the analysis or reporting may be different now.  Using these as examples may help authors to make better selections when using prior work as "templates" for their own study designs, or gain an understanding of what parts may need to be updated given current preferred practices.  I believe there is tremendous power is showing not only positive but also negative examples.

Also, I'd like to suggest that as a companion to a primer document for authors and reviewers, we may also consider producing a series of short humourous videos.  For authors "have you ever written...?", or reviewers "have you ever read...?".  Half poking fun at (to help personalize), but also constructively using the scenario as a platform for discussion.  If we can broach this subject in an entertaining way, we may gain more traction and avoid coming off as some kind of stats-police.  

- Mike



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Pierre Dragicevic

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May 31, 2016, 6:26:24 PM5/31/16
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Those are all great comments.

I also find that a reviewer's guide would more interesting and perhaps more needed.

An author's guide would be great of course and it's a common request, but an enormous amount of time and energy has been already spent on this issue: hundreds of textbooks, methodology papers, etc., all of these are author's guides. We're not short of resources authors can read if they're willing to invest time. In HCI recently, there's the book edited by Judy and Maurits for example, or Matt et al's paper on Bayesian methods. It's overwhelming of course, and many would love a document that summarizes it all for them, but the truth is that there are about as many approaches and analysis/reporting styles as books and articles. I don't think it will be easy to come up with an author guide that's approved by all the people in this mailing list (let alone the entire CHI community) while not being so unspecific as to be mostly useless.

I do think there are ways to come up with an author's guide that's novel and useful if it focuses on a specific theme, e.g., on how to do transparent statistics. Ideas like using an example-based approach, or adopting and entertaining/funny tone are also definitely worth exploring. But perhaps this wouldn't require many authors to be involved, and wouldn't need a stamp of approval from the CHI PC.

The relatively large number of people involved in this group (75 members for now) is however a unique opportunity to collectively agree on a reviewer's guide. We may all have different ways of doing stats and disagree on some specific questions, but it doesn't matter, because as I said the guide could just be a union of different common practices. It doesn't mean we'll adopt an "anything goes" approach as we can decide to, e.g., endorse a practice only if it's advocated by at least a few papers from the methodology literature. Some sources say remove outliers, some sources say don't bother, some say systematically correct for multiplicity, some say it's not that simple, some say test for normality, some say it's silly, etc,. I think a good guide should acknowledge all issues that are difficult and controversial, and suggest the reviewer to look elsewhere if possible.

This goes very much in the direction of what David suggests, in trying to make the review process more fair and less random (also see our alt.chi paper on this). I like the law analogy and I completely agree with the philosophy of "placing the burden on the reviewer to establish the harm to the author’s conclusions". As far as I'm concerned, David totally nails it. What should matter to a reviewer is the validity (or perhaps the credibility, expressed on a continuous subjective scale) of the author's conclusions, and if a reviewer isn't sure about some particular aspect of a method (which is fine), they could simply abstain from negatively commenting on it.

At the same time, I agree with Mike that a good reviewer's guide should promote best practices, and I'm not quite sure how to do this as well. Perhaps by using a sort of ordering or rating system? Sharing data is a ++, planned analyses are a +++, while reporting statistical significance without means / effect sizes would be a --? Not sure...

A reviewer's guide like this could act as a guide for authors as well, as they could read it and get a sense of the "rules" by which they can play. But in contrast with an author's guide a la APA, this wouldn't police authors or force them to go in a unique direction, and wouldn't necessarily need to put the bar much higher. CHI authors could continue to use their traditional methods if they wish to, but they could also decide to explore less familiar methods without the fear of being punished for doing weird things -- hopefully, they would even be encouraged to do so. Everyone would be given the opportunity to improve and polish their methods at their own pace, and choose their favorite school of thought.

Concerning obsoleting practices, it's a good idea but I agree with Sean that we should try to be soft, as it's unlikely that the CHI community will be open to any type of strong prescription, even if it's delayed in time. Alternatively, a guide could label some controversial questions as unresolved, and update them the next year if a consensus is reached. Until a question is unresolved, reviewers would be discouraged to force their personal opinion on the matter in their own reviews, but they would encouraged to contribute their thoughts for the next version of the guide.

Concerning length, short is good, but if we want it to be more detailed, a FAQ format should be easy to process.

Pierre

Jessica Hullman

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May 31, 2016, 10:01:37 PM5/31/16
to Pierre Dragicevic, Mike Glueck, David Lovis-McMahon, Transparent Statistics in HCI, Sean Munson, ROBERTSON Judy
Pierre's last comments have me envisioning a reviewer checklist of sorts, where each item is framed as a question or statement (e.g., 'Does the paper include claims that results from significance testing support the authors' hypothesis?', 'Does the summary of results discuss effect size?' etc). Depending on whether the answer is no or yes, the reviewer could either move on to the next point or look up that item in an appendix that gives more detail on why its a problem, and then bring up that point in their review. In contrast to a rating mechanism, which could be hard to validate, a checklist format could organize the reviewing process while educating the reviewer on finer points they aren't familiar with. People may be motivated to use a checklist if it makes reviewing easier/quicker (i.e., here's all the things I need to check, and for every violation I have a useful point to make in my review). How much the checklist should be targeted just to statistics versus to other aspects of experimental design/explication is an open question, as is what specific items it should include, but I could see it encompassing both problems (like the examples above) but also providing a way for a reviewer to classify whether they are dealing with a specific type of analysis (this is a Bayesian hierarchical model, here's what I need to know about what to look for to evaluate it etc) 

Jessica

Jessica Hullman
Assistant Professor, Information School
Adjunct Assistant Professor, Computer Science & Engineering
University of Washington

Judy Kay

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May 31, 2016, 10:08:33 PM5/31/16
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On a slightly different tack, can we identify some exemplar papers and to add some commentary of why they were chosen as exemplars. 

People learn well from concrete examples.

Though the down-side is that any one paper is clearly very specific.

But if people had some papers to suggest, I am sure people would find them very helpful.

On 1 June 2016 at 12:01, Jessica Hullman <jessica...@gmail.com> wrote:
Pierre's last comments have me envisioning a reviewer checklist of sorts, where each item is framed as a question or statement (e.g., 'Does the paper include claims that results from significance testing support the authors' hypothesis?', 'Does the summary of results discuss effect size?' etc). Depending on whether the answer is no or yes, the reviewer could either move on to the next point or look up that item in an appendix that gives more detail on why its a problem, and then bring up that point in their review. In contrast to a rating mechanism, which could be hard to validate, a checklist format could organize the reviewing process while educating the reviewer on finer points they aren't familiar with. People may be motivated to use a checklist if it makes reviewing easier/quicker (i.e., here's all the things I need to check, and for every violation I have a useful point to make in my review). How much the checklist should be targeted just to statistics versus to other aspects of experimental design/explication is an open question, as is what specific items it should include, but I could see it encompassing both problems (like the examples above) but also providing a way for a reviewer to classify whether they are dealing with a specific type of analysis (this is a Bayesian hierarchical model, here's what I need to know about what to look for to evaluate it etc) 

Jessica

Jessica Hullman
Assistant Professor, Information School
Adjunct Assistant Professor, Computer Science & Engineering
University of Washington
On Tue, May 31, 2016 at 3:25 PM, Pierre Dragicevic <pierre....@gmail.com> wrote:

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Mike Glueck

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Jun 1, 2016, 11:51:44 AM6/1/16
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David Lovis-McMahon

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Jun 1, 2016, 1:35:50 PM6/1/16
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Just a quick riff with a brief comment somewhat salient comment. 

Funny enough, just got published in Psych Science last week Marginally Significant Effects as Evidence for Hypotheses

There is some interesting information about reporting trends but since their data is available up on OSF, I remixed it to look at a couple of different ways.

(Note the apparent coding error in Developmental in 1980.)



And what really stood out to me was the apparent changes over three decades in Social Psychology.


In the 1990's the density of p-values was pretty well dispersed across the range of p-values. Then in 2000 the distribution shifted in pretty strongly to a peak at .10 before swinging the other direction to a peak at ..05+ in 2010. I'd have to ask around but I wonder if this was a byproduct of guidelines changing at the journal regarding "marginal p-values." 




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Pierre Dragicevic

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Jun 1, 2016, 1:37:39 PM6/1/16
to Mike Glueck, Judy Kay, Jessica Hullman, David Lovis-McMahon, Transparent Statistics in HCI, Sean Munson, ROBERTSON Judy
I saw this post before and the quotes are hilarious.

It's also a good example of what I was talking about, because the post's author (and he's far from being the only one) explains that the only correct approach is to report p as either significant or not significant, while in reality sources exist that recommend the exact opposite (including the Greenland et al paper sent by David, the APA guidelines, and back to Fisher himself).

Pierre

David Lovis-McMahon

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Jun 1, 2016, 1:38:08 PM6/1/16
to Transparent Statistics in HCI, jessica...@gmail.com, pierre....@gmail.com, mikeg...@gmail.com, dlo...@gmail.com, smu...@uw.edu, judy.ro...@ed.ac.uk, judy...@sydney.edu.au
I think making note of exemplar papers can have some definite benefits. One way could be to use them as the jumping off point for specific examples in something like a faq. Where you take a brief summary of the article and either real data or simulated data and use it to demonstrate a particular analysis technique. Then suggest the original exemplar article for further reading.

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David Lovis-McMahon

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Jun 1, 2016, 1:43:59 PM6/1/16
to Transparent Statistics in HCI, mikeg...@gmail.com, dlo...@gmail.com, smu...@uw.edu, judy.ro...@ed.ac.uk
Perhaps it would be beneficial to split up the Reviewer's Guide, the Author's Guide, and a FAQ that integrates the two view points? The two general guides can provide a sort of short manifesto of what the aims of transparency are for both authors and reviewers with specific examples, best practices, and other information provided in an online FAQ / community?

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