Sensitivity and specificity
Statistical measures of the performance of a binary classification test In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive.Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. A test which reliably detects the presence of a condition, resulting in a high number of true positives and low number of false negatives, will have a high sensitivity. Application to screening study[edit] Definition[edit] Sensitivity[edit]
Unease in Hawaii’s Cornfields
Cory Lum for The New York Times A research scientist at Pioneer in a Dupont Pioneer cornfield near Koloa, Kauai. The county is considering limits on growers. Toby Hoogs for The New York Times “Without G.M.O., there would be no papaya in Hawaii,” said Eric Weinert, general manager of Hawaii operations for Calavo Growers, a papaya packer. The state has become a hub for the development of genetically engineered corn and other crops that are sold to farmers around the globe. But activists opposed to biotech crops have joined with residents who say the corn farms expose them to dust and pesticides, and they are trying to drive the companies away, or at least rein them in. The companies counter that their operations are safe and that the industry is essential to Hawaii’s economy. In the last two weeks, legislative committees on the islands of Kauai and Hawaii have approved proposed ordinances that would restrict the ability of the seed companies to operate. Ms. On some occasions, Ms.
An Intuitive (and Short) Explanation of Bayes’ Theorem
Bayes’ theorem was the subject of a detailed article. The essay is good, but over 15,000 words long — here’s the condensed version for Bayesian newcomers like myself: Tests are not the event. Bayes’ theorem converts the results from your test into the real probability of the event. Correct for measurement errors. Anatomy of a Test The article describes a cancer testing scenario: 1% of women have breast cancer (and therefore 99% do not).80% of mammograms detect breast cancer when it is there (and therefore 20% miss it).9.6% of mammograms detect breast cancer when it’s not there (and therefore 90.4% correctly return a negative result). Put in a table, the probabilities look like this: How do we read it? 1% of people have cancerIf you already have cancer, you are in the first column. How Accurate Is The Test? Now suppose you get a positive test result. Here’s how I think about it: Ok, we got a positive result. The table looks like this: And what was the question again? Bayes’ Theorem Have fun!
Basu's theorem
From Wikipedia, the free encyclopedia Theorem in statistics It is often used in statistics as a tool to prove independence of two statistics, by first demonstrating one is complete sufficient and the other is ancillary, then appealing to the theorem.[2] An example of this is to show that the sample mean and sample variance of a normal distribution are independent statistics, which is done in the Example section below. This property (independence of sample mean and sample variance) characterizes normal distributions. Statement[edit] Let be a family of distributions on a measurable space and a statistic maps from to some measurable space . is a boundedly complete sufficient statistic for , and is ancillary to , then conditional on is independent of . Proof[edit] and be the marginal distributions of respectively. Denote by the preimage of a set under the map . we have The distribution does not depend on because is ancillary. is sufficient. Note the integrand (the function inside the integral) is a function of . .
Metascience
Scientific study of science Metascience (also known as meta-research) is the use of scientific methodology to study science itself. Metascience seeks to increase the quality of scientific research while reducing inefficiency. It is also known as "research on research" and "the science of science", as it uses research methods to study how research is done and find where improvements can be made. Metascience concerns itself with all fields of research and has been described as "a bird's eye view of science".[1] In the words of John Ioannidis, "Science is the best thing that has happened to human beings ... but we can do it better In 1966, an early meta-research paper examined the statistical methods of 295 papers published in ten high-profile medical journals. History[edit] Fields and topics of meta-research[edit] An exemplary visualization of a conception of scientific knowledge generation structured by layers, with the "Institution of Science" being the subject of metascience. Methods[edit]
My Father Says He’s a ‘Targeted Individual.’ Maybe We All Are
Addiction can't always be cured so let's focus on quality of life
Alcohol and substance abuse costs the Australian economy A$24.5bn a year. The human toll from accidents, overdoses, chronic disease, violence, mental illness and family disruption, however, is immeasurable. Modern, evidence-based policy responses to addiction focus on treatment, where patients aim to withdraw from drugs through therapy and medications. Harm-minimisation strategies such as the supply of clean needles and syringes and the prescribing of substitution medications are also key elements of Australia’s drug strategy. But while these measures play an important role in how we deal with addiction, little attention is paid to what happens next. Regardless of how good the treatment is, half to three-quarters of drug users relapse. This is why so many professionals and policy makers, as well as people with addictions and their family members, are turning to the recovery movement – as many in the mental health sector have done in recent years with great success.
The Suzanne Moore-Julie Burchill uproar shows how utterly bonkers parts of the radical Left are at the moment
Photoshopper's note: Dan insisted on being 'shopped as Javert for this one. Last night I went to see Les Misérables, and nearly disgraced myself. As the ensemble broke into their first stirring rendition of “Do You Hear The People Sing” – and all around me wept proud tears of solidarity – I almost burst out laughing. It wasn’t that I was belittling Hugh Jackman and Eddie Redmayne’s revolutionary ardour, though Russell Crowe’s Javert did strike me as just the sort of man who knows how to take tough choices in an age of austerity. Instead I was reminded of the time I was working for the GMB union, and we decided to use “The People’s Song” to open our annual congress. Every year we’d begin with Jerusalem, and for once we thought we’d let our hair down a bit. Just like those manning the Parisian barricades in 1832, we stood accused of treachery. The Left detests a traitor. No sooner had Moore been officially found to be in league with the devil than it was Julie Burchill’s turn.
