| > Personally, I suspect it is not an accidental outlier, but given that it does not produce much distortion in the overall trend, I am less inclined to see the 0.05 threshold (actual or perceived) as a problem than I did before I saw this chart. Don't be fooled by the line someone drew on the chart. There's no particular reason to view this as a smooth nonlinear relationship except that somebody clearly wanted you to do that when they prepared the chart. I could describe the same data, with different graphical aids, as: - uniform distribution ("75 papers") between an eyeballed p < .02 and p < .05 - large spike ("95 papers") at exactly p = 0.4999 - sharp decline between p < .05 and p < .06 - uniform distribution ("19 papers") from p < .06 to p < .10 - bizarre, elevated sawtooth distribution between p < .01 and p < .02 And if I describe it that way, the spike at .05 is having exactly the effect you'd expect, drawing papers away from their rightful place somewhere above .05. If the p-value chart were a histogram like all the others instead of a scatterplot with a misleading visual aid, it would look pretty similar to the other charts. |
I think we are both, in our own ways, making the point that there is more going on here than the spike just below 0.05 - namely, the regular pattern that you identified in your original post. If we differ, it seems to be because I think it is explicable.
WRT p-values of 0.05: I almost, but did not, say that if you curve-fitted above and below 0.05 independently, there would be a gap between the two, and maybe even if you left out the value immediately below 0.05. No doubt that would also happen for other values, but I am guessing that this gap would peak at 0.05. If I have time in the near future, I may try it. If you do, and find that I am wrong, I will be happy to recant.