I'm the author of this paper.

I’ve avoided engaging in forum discussions, but Aaronji’s comments caught my eye, and I couldn’t help but respond.

- Full data, analysis methods, source code etc are available at

https://code.soundsoftware.ac.uk/projects/hi-res-meta-analysis . I encourage anyone who is interested to perform their own analysis, and I will happily answer any questions. I expect that others may be more rigorous, or may uncover other interesting information that I overlooked. I’ll also try to answer any comments posted on the paper’s forum at

https://secure.aes.org/forum/pubs/journal/?ID=591 - I consulted with statisticians and meta-analysis experts at various stages throughout the preparation of the paper. I would have liked a co-author with expertise in those areas, but the people I asked were unavailable.

- The Appendix was not included in the original submission, but was requested by one of the reviewers. I believe this request was correct, since the readers of the AES journal, including those who frequently apply statistical techniques to their data, are generally not familiar with meta-analysis and the techniques applied in that field.

- I’m aware of the importance of homogeneity, and the heterogeneity issues here are more serious than those that would typically be found in medical research, and a world apart from formal clinical trials. However, meta-analysis has been successfully applied to social and behavioural science research with far more heterogeneity problems than those seen here. Anyway, this is a judgement call. So the approach I took was to use all possible studies (for which I could do inverse variance analysis), and then do sensitivity or subgroup analysis on more homogeneous subsets of the data.

- bias. This made me laugh at first since in relation to this paper I’ve been accused of bias from all sides. Before beginning the study, I did not have a strong opinion either way as to whether differences could be perceived. But I could easily be fooling myself. So I committed to publishing all results, regardless of outcome. And again, I included all possible studies, even if I thought they were problematic, then did further analysis looking at alternative choices. I also decided that any choices regarding analysis or transformation of data would be made a priori, regardless of the result of that choice. However, I wrote the paper once all the analysis had been done, and so my writing style may reflect my knowledge of the conclusions.

- I agree that the work would have been improved by using an approach specific to binomial distributions. However, for much of the analysis, the normal approximation is justified. As for independence in the binomial test, under the null hypothesis every randomised trial would be uncorrelated, regardless of whether they involved the same participant or same study (think guessing a truly random coin toss). I also agree that the aggregate binomial test is not appropriate for meta-analysis. It was included only for completeness along with the binomial values for the individual studies in Section 2, and not used as part of the meta-analysis in Section 3.

- For King 2012 (the ‘closer to live’ study), it could have been either excluded it completely, treated higher preference rating as discrimination (which is fraught with issues) or treated closer to live as successful discrimination. Since the live feed was provided as a reference stimulus, similar to many other multistimulus evaluation studies, and the intention of the 192 kHz feed was to be ‘closer to live’ even if not perceived, this seemed a logical approach. Again, this decision was made a priori, in an attempt to minimize any of my own biases influencing the outcome.

- The studies were mainly from the audio engineering discipline and had a strong tendency to expressing and considering results (effect sizes) as means rather than proportions, and expressing probabilities as percentages. This is reflected in the paper, though better editing on my part would have resulted in more consistency with the notation of p values. I could have also performed sensitivity analysis where results were considered as odds ratios. But at some point, one has to stop looking at every variation and just submit the paper.

- The structure of the paper is in-line with the structure of most engineering papers (including IEEE). As such, it looks very different from the structure of papers in medical journals and other places where a lot of meta-analysis is published.

- The standard explanation for the publication bias problem was mentioned several times. The beginning of Section 3.6 first presents it. Figure 3 shows that the apparent evidence of publication bias from the funnel plot mostly goes away when subgrouping is applied. However, it then goes on to state "publication bias may still be a factor" and in Conclusion, "still a potential for reporting bias. That is, smaller studies that did not show an ability to discriminate high resolution content may not have been published."