ScienceGeist

Proposal to change widely accepted p-value threshold stirs reproducibility debate.

Significance thresholds and the crisis of unreplicable research

Today we’re rolling out new features in Sheets that make it even easier for you to visualize and share your data, and find insights your teams can act on.

"When someone is honestly 55 percent right, that’s very good and there’s no use wrangling. And if someone is 60 percent right, it’s wonderful, it’s great luck, and let him thank God."

Take proposals that should never have been submitted out of the figures, and the chances of winning funding look a lot brighter.

Significance thresholds and the crisis of unreplicable research

Published P-values provide a window into the global enterprise of medical research. The aim of this study was to use the distribution of published P-values to estimate the relative frequencies of null and alternative hypotheses and to seek irregularities suggestive of publication bias.

This is the second part in a series on how we edit science, looking at hypothesis testing, the problem of p-hacking and how the peer review process works.

Academic psychology and medical testing are both dogged by unreliability. The reason is clear: we got probability wrong.

The replication crisis in science is largely attributable to a mismatch in our expectations of how often findings should replicate and how difficult it is to actually discover true findings in certain fields.

On the many ways to say the same thing.

The problem with p-values.

Ridding science of shoddy statistics will require scrutiny of every step, not merely the last one.

H-index, citations, self-citations and other figures about Switzerland, based on Scopus data.