Yes, but one should be on Mr Peanutbutter's head instead of Princess Caroline's

which is such a shame because there really should be more evidence for what is and isn't placebos

And it is a terrible thing for science and contributes greatly to the crisis of irreproducibility plaguing multiple fields.

If 1000 researchers study the same thing, and 950 of them find insignificant results and don't publish, and 50 of them publish their significant (95% confidence) results - we have collectively deluded ourselves into accepting spurious conclusions.

This is a massive problem that is rarely acknowledged and even more rarely discussed.

Z value (also known as z-score) is the distance (signed) between your model and a prediction.

If your model is a mean (the average), the z-scores are the set of differences between the mean and the values used to compose the mean.

If your model is a regression (relating, say, two variables relating x and y), then the z-score is the difference between the regression line and the values used to fit the regression.

My limited knowledge on this subject: The z-score is how many standard deviations you are from the mean.

In statistical analysis, things are often evaluated against a p (probability) of 0.05 (or 5%), which also corresponds to a z-score of 1.96 (or roughly 2).

So, when you're looking at your data, things with a z score >2 or <2 would correspond to findings that are "statistically significant," in that you're at least 95% sure that your findings aren't due to random chance.

As others here have pointed out, z-scores closer to 0 would correspond to findings where they couldn't be confident that whatever was being tested was any different than the control, akin to a boring paper which wouldn't be published. "We tried some stuff but idk, didn't seem to make a difference." But it could also make for an interesting paper, "We tried putting healing crystals above cancer patients but it didn't seem to make any difference."

A Z score is a type of airplane, I believe.

As I understand it, the data there is the histogram of z-value observed by some census of published papers.

They should make a normal curve, but the publishing process is biased. (On the best case, otherwise the research process would be biased.)

I came here with the same question but now I realize that if I ask it I will only get replies explaining me what Z-score is and not Z-score of what. So I will just assume it is sth akin to h-index. Still does not make much sense to me as to why average h-index papers "don't survive" (i.e get rejected because no one is interested lets say) where as negative ones do.

could we pick somewhere that's not a splash zone?