Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with precision
Search options answers only
not deleted
user 805
Precision is about variability while accuracy (in contrast to precision) is about bias. This tag pertains to measurement or estimation; use [precision-recall] when talking about classifiers.
106
votes
Accepted
How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)
There's a good reason for it.
The value can be found via noquote(unlist(format(.Machine)))
double.eps double.neg.eps double.xmin
2.220446e-16 1.110223e-1 …
- 291k
9
votes
Are there parameters where a biased estimator is considered "better" than the unbiased estim...
There are numerous examples where the MLE has smaller mean square error (MSE) than the best available unbiased estimator (though often there are estimators with smaller MSE still). The "standard" exam …
- 291k