Reposted by A domesticated wildcat
I don’t think everyone realizes how bad things are for US science:
For the fiscal year that just ended, the Trump regime withheld $1.4 billion from the National Science Foundation’s research budget, which is about 1/5 of its primary research budget.
nytimes.com
National Science Foundation Diverted $1.4 Billion in Science Funds Mandated by Congress (Gift Article)
The move, affecting about one-fifth of the National Science Foundation’s primary research budget, is the latest sign of how the White House is asserting more control over grants.
![Abstract of our article "The Difference Between “Replicable” and “Not replicable” is not Itself Scientifically Replicable"
Replication studies estimate the replicability rate of scientific results by aggregating binary verdicts of experiments. Exact replications are rarely attainable, so most replication sequences are non-exact. Experiments differ in ways that matter and do not share a single common data-generating process. We formalize two statistical interpretations of this non-exactness. In a shared latent rate model (benchmark), experiments are exchangeable and depend on a common
random replicability rate. In a conditionally independent rates model (operational), each experiment has its own replicability rate drawn independently from a population distribution. Under the shared latent rate model, even small variability among replicability rates induces an irreducible variance floor on the estimated mean replicability rate that cannot be eliminated by adding more replications. Under the conditionally independent rates model, the degree of non-exactness is not identifiable from standard replication data, because one binary verdict per experiment contains no information about between-experiment
heterogeneity. Researchers therefore cannot tell which precision regime they are
operating in or whether high- and low-replicability sequences can be distinguished in principle. As a result, the usual data structure of one binary verdict per experiment cannot support reliable demarcation between “replicable” and “not
replicable” results and systematically understates uncertainty, making high- and
low-replicability sequences appear discriminable when they are not. [...] Our results imply that replicability rate is not a reliable demarcation criterion for scientific results. The replication crisis, if there is one, cannot be established by the methods used to declare it.
Keywords— Replication, replicability rate, heterogeneity, non-exactness, demarcation](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:yha64k6gbnmndrc2u6bxpmzo/bafkreicy3m4lxez2w4lykusv6ofykfx73w4ym7jjwonz5nh32ekwuzgqqq)
