
Commentators agree with me that statistical significance does not betoken replicability, but not for the reasons I give. Chow (1998) defends the use of significance tests by arguing that they are useful for assessing the role of chance in findings. I dispute this by pointing out that the Type II error rate may be as high as 1 - ct. Falk (1998) defends Bayesian inference, which was impugned by me. She also makes a case for the use of replications for the purpose of testing hypotheses. I complain that the supporters of Bayesian inference have not explained adequately what its purpose or role in science is, and for this reason it is an impediment to understanding in this area. I also dispute Falk's view of the role of replications in that I would require a successful replication to be one that produces an effect that is clearly discernible, meaning the effect is consistently noticeable with the aid of nothing more than descriptive statistics.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
